Use of standardized nursing terminologies in electronic health records for oncology care: the impact of NANDA-I, NOC, and NIC

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1 University of Iowa Iowa Research Online Theses and Dissertations 2012 Use of standardized nursing terminologies in electronic health records for oncology care: the impact of NANDA-I, NOC, and NIC Hui-Chen Tseng University of Iowa Copyright 2012 Hui-Chen Tseng This dissertation is available at Iowa Research Online: Recommended Citation Tseng, Hui-Chen. "Use of standardized nursing terminologies in electronic health records for oncology care: the impact of NANDA-I, NOC, and NIC." PhD (Doctor of Philosophy) thesis, University of Iowa, Follow this and additional works at: Part of the Nursing Commons

2 USE OF STANDARDIZED NURSING TERMINOLOGIES IN ELECTRONIC HEALTH RECORDS FOR ONCOLOGY CARE: THE IMPACT OF NANDA-I, NOC, AND NIC by Hui-Chen Tseng An Abstract Of a thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Nursing in the Graduate College of The University of Iowa July 2012 Thesis Supervisor: Associate Professor Sue Moorhead

3 1 ABSTRACT The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian s structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain Pain Level Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and

4 2 outcomes for oncology nurses can direct nursing care in clinical practice and provide direction for future research tot targets areas of high impact and guide education and evaluation of nurse competencies. Abstract Approved: Thesis Supervisor Title and Department Date

5 USE OF STANDARDIZED NURSING TERMINOLOGIES IN ELECTRONIC HEALTH RECORDS FOR ONCOLOGY CARE: THE IMPACT OF NANDA-I, NOC, AND NIC by Hui-Chen Tseng A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Nursing in the Graduate College of The University of Iowa July 2012 Thesis Supervisor: Associate Professor Sue Moorhead

6 Copyright by HUI-CHEN TSENG 2012 All Rights Reserved

7 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL PH.D. THESIS This is to certify that the Ph.D. thesis of Hui-Chen Tseng has been approved by the Examining Committee for the thesis requirement for the Doctor of Philosophy degree in Nursing at the July 2012 graduation. Thesis Committee: Sue Moorhead, Thesis Supervisor Elizabeth Swanson Timothy Ansley Jane Brokel Howard Butcher

8 To My Family, My Grandmother ( 曾林水香 ), Father ( 曾茂田 ), Mother ( 曾廖梅英 ), Brothers ( 曾健華, 曾健禹, 曾健政 ), and Sisters-in-Laws ( 吳靜芳, 張雅如, 張簡明芳 ) ii

9 ACKNOWLEDGMENTS It is hard to express my gratitude to my PhD advisor, Professor Sue Moorhead. Without her inspirational guidance, her enthusiasm, her encouragement, her unselfish help, I could have never finished my doctoral work at the University of Iowa. My special thanks go also to the members of my advisory committee for their guidance and helpful discussions. I am grateful for the support from the hospital, Department of Nursing, the Cancer Center, and the Institute for Clinical and Translational Science. I want to thank the Nursing Informatics specialist, Teresa Clark in the Department of Nursing, the health information technicians, Julie Risinger and Tania Viet in the Cancer Center, the data analyst, Lisa Sturtz in the Joint Office for Compliance, and all the team that assisted me during the data collection process. I would like to thank Elena Perkhounkova, who trained me to manage the data for data analysis. I would like to express my thankfulness to Sharon Sweeney, my walking dictionary for NANDA-I, NIC, and NOC. I thank my doctoral classmates for their support during my doctoral study. Finally, I want to express my appreciation to my family for their love, understanding, patience, endless support, and never failing faith in me. iii

10 ABSTRACT The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian s structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain Pain Level Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and iv

11 outcomes for oncology nurses can direct nursing care in clinical practice and provide direction for future research tot targets areas of high impact and guide education and evaluation of nurse competencies. v

12 TABLE OF CONTENTS LIST OF TABLES... ix LIST OF FIGURES... xi CHAPTER I...1 Background and Significance...3 Problem Statement and Purpose of the Study...6 Conceptual Model...7 Research Questions...8 Definitions of Variables...10 Linkages of NNN...10 Nursing-Sensitive Patient Outcomes...11 Planned Nursing Care...12 Length of Stay (LOS)...13 Oncology Nursing Care...13 Summary...14 CHAPTER II REVIEW OF THE LITERATURE...15 Cost and Quality in Oncology Nursing Care...15 Nursing Nomenclature...16 NANDA International, NOC, and NIC: The NNN Linkages...17 A Brief Description of the NNN Linkages...17 NANDA International...17 Nursing Interventions Classification...18 Nursing Outcomes Classification...18 The NNN Linkages...18 Three Theory Explanations of the NNN Linkage...20 Standardized Nursing Terminologies in EHRs...21 Meaningful Use for the Nursing Profession...21 Nursing s Contribution to Quality Patient Care...22 Benefits of SNTs in EHRs...23 Effective Communication and Quality of Documentation...24 Comprehensive Planned Care...25 Evidence-Based Practice (EBP)...25 Quality Improvement, Patient Satisfaction, Cost Reduction and a Shorter LOS...26 Low Adoption Rate of SNTs in EHRs...26 Nursing Research Focused on Health Systems, Technology, and Nursing Specialties...27 Oncology Nursing Care...29 Cancer Symptom and Symptom Cluster...29 Characteristics of Patients and Oncology Nursing Care...31 NOC and Measurement of Symptom Clusters...31 Cost-Effectiveness Research...32 LOS in Oncology Units...33 Defining Nursing Knowledge...34 NANDA-I, NIC, and NOC in Oncology Nursing Care...34 Summary...35 CHAPTER III RESEARCH DESIGN, METHODS, AND DATA ANALYSIS...37 vi

13 Research Design...37 Setting and Sample...38 Variables and Measures...39 Data Collection and Management...40 Data Analysis and Interpretation...41 Human Subjects...45 Summary...46 CHAPTER IV DATA ANALYSIS AND RESULTS...48 Sample...49 Analysis of the Research Questions...49 Demographics of Patients...50 Disease Information...50 Hospitalization Information...52 Type of Insurance...52 NANDA-I Nursing Diagnoses: All Units...59 NIC Interventions: Domains and Classes...61 NIC Interventions: All Units...63 NOC Outcomes: Domains and Classes...63 NOC Outcomes: All Units...65 NANDA-I, NIC, and NOC by Unit...67 NANDA-I Nursing Diagnoses by Unit...67 NIC Interventions by Units...68 NOC Outcomes by Unit...69 NANDA-I, NIC, and NOC by LOS...70 NANDA-I Nursing Diagnoses by LOS...71 NIC Interventions by LOS...72 NOC Outcomes by LOS...73 Pattern of NNN Linkages...75 Patterns of Nursing Diagnoses (NANDA-I) Combinations...78 Outcome Change Scores for Pain Level...83 Summary...87 CHAPTER V DISCUSSION AND CONCLUSION Overview of Study Findings Type of Unit, Age, Treatment Associated with LOS Top Ranking of Nursing Diagnoses (NANDA-I) Acute Pain Top Ranking of Nursing Interventions (NIC) Pain Management Top Ranking of Nursing-Sensitive Patient Outcomes (NOC) Pain Level Top Linkages of NANDA-I, NOC, and NIC (NNN) Acute Pain Pain Level Pain Management Patterns of NANDA-I Diagnoses, NIC Interventions, Outcome Change Scores: Pain Level Outcome Change Scores Mostly Maintained or Improved Outcome Ratings Use of Outcome Change Scores Use of Outcome Ratings at Admission Use of Outcome Ratings at Discharge Study Limitations Population Representation One Site Sample vii

14 Data Data Quality Sample Size for Outcome Change Scores Study Design and Tools SNTs in EHRs for Oncology Population Study Variables Implications for Nursing Practice Implications for Nursing Education Implications for the Health Care System and Health Policy Use of Different Resources SNTs in EHRs and Data Warehouses Modify Study Design Inclusion of Dose of Nursing Intervention Development of Optimum Ranges for Outcome Ratings Pattern of NOC Outcome Change Scores Compatibility of NOC Outcome Ratings and Other Scales Conclusion Summary APPENDIX A DEFINITIONS APPENDIX B NANDA-I DIAGNOSIS: ACUTE PAIN APPENDIX C NIC INTERVENTIONS: PAIN MANAGEMENT APPENDIX D NOC OUTCOME: PAIN LEVEL APPENDIX E NURSING DIAGNOSES: LEUKEMIA PATIENTS APPENDIX F LINKS OF NANDA-I AND NIC APPENDIX G LINKS OF NANDA-I AND NOC APPENDIX H LINKS OF NOC AND NIC viii

15 LIST OF TABLES Table 1 Demographics, Disease Information, Hospitalization Information, and Type of Insurance...91 Table 2 Length of Stay (LOS) and Its Relationship with Type of Unit, Age Group, Race, Medical Treatment Group, and Type of Insurance...96 Table 3 Count of Nursing Diagnoses (NANDA-I), Nursing Interventions (NIC), and Nursing-Sensitive Patient Outcomes (NOC) Per Person (N=2,237)...97 Table 4 Domains and Classes of the Unique 88 Nursing Diagnoses (NANDA-I)...98 Table 5 Ranking of Nursing Diagnoses (NANDA-I) for All Units Table 6 Domain and Class of the Nursing Interventions (NIC) Table 7 Ranking of NIC Nursing Interventions for All Units Table 8 Domains and Classes of Nursing Outcomes (NOC) Table 9 Ranking of Nursing -Sensitive Patient Outcomes (NOC) for All Units Table 10 Ranking of NANDA-I (Nursing Diagnoses) by Unit Table 11 Ranking of Nursing Diagnoses (NANDA-I) Found Significantly Different by Unit Table 12 Ranking of Nursing Interventions (NIC) by Unit Table 13 Ranking of Nursing Interventions (NIC) Found Significantly Different by Unit Table 14 Ranking of Nursing-Sensitive Patient Outcomes (NOC) by Unit Table 15 The Top Ranking of Nursing Outcomes (NOC) Found Significantly Different by Unit Table 16 Ranking of Nursing Diagnoses (NANDA-I) by Length of Stay (LOS) Table 17 Ranking of Nursing Diagnoses (NANDA-I) Found Significantly Different by LOS Table 18 Ranking of Nursing Interventions (NIC) by Length of Stay (LOS) Table 19 Ranking of NIC Outcomes Found Significantly Different by LOS Table 20 Ranking of Nursing Outcomes (NOC) by Length of Stay (LOS) Table 21 Ranking of NOC Outcomes Found Significantly Different by LOS ix

16 Table 22 The Top Ten linkages of Nursing Diagnoses (NANDA-I), Nursing Interventions (NIC), and Nursing-Sensitive Patient Outcomes (NOC) Table 23 Patterns of Nursing Diagnoses (NANDA-I) Combinations Table 24 Pattern of Nursing Interventions (NIC) Combinations Table 25 Pattern of Nursing-Sensitive Patient Outcomes (NOC) Combinations Table 26 Distribution of Outcome Change Scores for Pain Level Table 27 Distribution of Outcome Change Scores for Infection Severity x

17 LIST OF FIGURES Figure 1 Donabedian's Model...8 Figure 2 The Distribution of Length of Stay (LOS) Before Data Transformation...55 Figure 3 The Distribution of Length of Stay (LOS) After Data Transformation...56 Figure 4 NOC Outcome Change Scores for Pain...89 Figure 5 NOC Outcome Change Scores for Infection Severity...90 xi

18 1 CHAPTER I INTRODUCTION A report of Institute of Medicine (IOM, 2011) revealed a call for nurses to take a leadership role to advance health care. This report stated that nurses roles, responsibilities and education should have a substantial change to meet the increasing demand for care from the reform of the health care system and to advance improvement in the complex health system. There are four key messages in the report: Nurses should practice to the full extent of their education and training. Nurses should achieve higher levels of education and training through an improved education system that promotes seamless academic progression. Nurses should be full partners, with physicians and other health care professionals, in redesigning health care in the United States. Effective workforce planning and policy making require better data collection and information infrastructure. All these messages describe nurses in a fundamental role for the transformation of the healthcare system that is seamless, affordable, focused on quality care and accessible to all and leads to improved health outcomes. A need of research to provide evidence supporting these four key messages and linking nursing care with quality patient care can never be over emphasized. Besides this, the nursing profession has been challenged to adapt to the impact of technology on the way nurses deliver patient care in each specialty. Recognizing the benefits of electronic health records (EHRs) to improve communication, coordination and quality care, the United States (U.S.) government has

19 2 created a nationwide initiative to promote the adoption and use of EHRs. One financial initiative to promote use of EHRs was entitled Meaningful Use and was a part of the American Recovery and Reinvestment Act of 2009 (ARRA). Demonstrating meaningful use is key to receiving financial incentives in the context of the EHR incentive program directed primarily at physician providers. To define Meaningful Use (MU), demographics and a set of measures need to be captured and reported in a structured manner, relating to the improvement of quality, efficiency, and patient safety in healthcare systems through use of certified EHR technology. It is expected that by 2014 each U.S. citizen will have the ability to communicate electronically through a national health information network. Nursing informatics specialists have been tailoring current EHRs to meet the MU incentive because of the need for evidence of the relationship between nursing care and patient outcomes (Madison & Staggers, 2011). Most nursing information systems are underdeveloped, without the capability to retrieve structured data, including quality of care measures related to nursing care such as nursing-sensitive patient outcomes. These challenges have presented an obstacle to meeting the MU incentive from a nursing perspective. In addition, current electronic nursing documentation systems are commonly fragmentary in design. For example, the separate applications for data entry, clinical notes, and care plans result in a dysfunctional documentation tool. Besides the unorganized design of the nursing documentation systems, the use of non-or-partially computerized nursing documentation or the lack of standardized languages in a variety of nursing documentation formats presents a significant challenge to capturing nursing care actually provided to patients and collecting information to improve quality. It is

20 3 imperative to process electronic nursing data that meets the criteria of MU to provide valuable insight into the nursing care provided to patients and to motivate vendors to advance the current nursing information systems to meet these needs. Because of the current limitations in EHRs, few studies have identified a complete set of nursing diagnoses, interventions, and outcomes using standardized nursing terminologies for patient populations with complex conditions such as oncology patients (Ralston, Coleman, Reid, Hadley & Larson, 2010). It is important to identify symptom clusters for inpatient oncology patients using electronic standardized nursing terminologies (SNTs) for nursing diagnoses, interventions, and outcomes. This study describes: 1) current demographic characteristics of oncology inpatients, 2) length of stay (LOS) based on age, gender, treatment, and type of insurance, 3) frequently used nursing diagnoses, interventions, and outcomes on four oncology units, and 4) outcome change scores associated with selected patient demographics. Background and Significance The cost of providing cancer care to patients diagnosed with a variety of types of cancer is a tremendous burden in the U.S. In 2010, an estimated 569,490 individuals were expected to die of cancer, more than 1,500 a day. Approaching these statistics from another perspective, cancer accounts for nearly 1 out of every 4 deaths and it is estimated that 1 out of 5 individuals will develop cancer by The National Institutes of Health estimated the overall cost of cancer in the U.S. in 2007 to be $219.2 billion and billion in In addition, the uninsured are likely to be diagnosed with cancer at a later stage, when treatment can be more expensive (American Cancer Society, 2010).

21 4 Nursing-sensitive patient outcomes have been emphasized in effectiveness research because of the importance of linking outcomes of patient care to nursing interventions. Over the last decade, numerous studies have focused on the organizational level to evaluate these relationships (Aiken, Clarke, Sloane, Sochalski, & Silber, 2002; Mark, Hughes, Belyea, Bacon, Chang, & Jones, 2008; Needleman, Buerhaus, Mattke, Stewart, & Zelevinsky, 2002). But few empirical studies have been conducted at the individual patient level and have included nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes from recognized American Nurses Association (ANA) classifications. Research using standardized nursing-sensitive patient outcomes at the individual patient level, is critical to help identify the contributions nursing makes to patient outcomes. In addition, previous research involving nursing-sensitive patient outcomes has tended to focus on negative outcomes such as falls, but a more complete description, including positive outcomes is critical to capture the contributions of nurses (Doran & Almost, 2003). The linkages of Nursing Outcomes Classification (NOC) with NANDA-I diagnoses and Nursing Interventions Classification (NIC) (NNN) have been published to assist nurses with clinical reasoning and decision-making. The use of the linkages of NNN in the U.S. and internationally by nurses has provided additional information on the relationships among diagnoses, interventions and outcomes, and has helped to identify variations in practice across settings (Johnson, Moorhead, Bulechek, Butcher, Maas, & Swanson, 2012). Computerized SNTs are needed for nationwide data aggregation for effectiveness research. However, confusion about which SNTs to use to capture these data, capability of computerized systems, acceptance of SNTs by practicing nurses, lack

22 5 of education on nursing terminologies, and implementation costs have impeded adoption of computerized nursing care planning and documentation systems using SNTs. Even though these issues exist, SNTs are very critical to nursing and research using SNTs in computerized nursing documentation systems is needed to demonstrate its advantages and subsequently increase the adoption and use of SNTs in nursing practice. A well-developed system that can link diagnoses, interventions, and outcomes improves the quality of documentation as well. But of critical importance is that linkages of nursing diagnoses, nursing interventions and nursing-sensitive patient outcomes in the computerized and standardized datasets mutually benefit nurses and patients and advance the development of the terminologies and further examine the relationships among research, theory, and practice. Additional research to identify the linkages for specific populations of patients and for specialty practice is relevant and can help build the knowledge base of nursing by capturing data from electronic health records. Nursing phenomena associated with the care of oncology patients has not been comprehensively captured or explained in current nursing literature within the context of standardized nursing terminologies. Efforts to capture oncology nurses practice patterns are an important endeavor to provide reliable and valid diagnoses, interventions, and outcomes for nurses to define their specialty and improve practice. The establishment of a comprehensive database in any specialty also facilitates the development of evidencebased practice and research. This is an important area because the care provided by nurses to cancer patients is complex and the educational needs for care continue to increase. Based on the statistics of cancer incidence, there is also a growing demand for nurses specializing in oncology.

23 6 Problem Statement and Purpose of the Study The validation of SNLs has been emphasized in various settings and selected populations (Johnson et al., 2012); however, research on oncology nursing practice using SNTs, directly retrieved from EHRs, is limited. Understanding the practice standards for oncology nursing care is important for researchers to examine due to the increasing numbers of cancer patients and the cost the care. Additionally, the study of SNTs within oncology nursing is critical in both education and clinical practice. Nursing professionals are striving to document their contribution to the quality of patient care. However, both the traditional non-formatted nursing documentation in EHRs and formatted nursing documentations in written charts have proven to be obstacles in demonstrating nursing s contribution to quality care in an efficient manner. SNTs in EHRs may highlight more of the nursing professional role, by offering efficient examination of data from large databases. As Meaningful Use for EHRs has been highlighted in recent years, measures for nursing care to improve the quality of patient care must be recognized as a part of Meaningful Use and other measures of quality. It is essential to measure and document nursing-sensitive patient outcomes. The findings of the improvement of nursing-sensitive patient outcomes, such as using NOC change scores from outcome ratings, provide evidence of nursing s contributions to the quality of patient care. Therefore, studies of SNTs in EHRs are required not only to demonstrate the Meaningful Use criterion but also to provide a solution for long existing issues on the invisibility of nursing care in health care delivery. There are five aims of this study: 1) to describe the characteristics of oncology patients admitted to a large academic health center in the Midwest; 2) to describe the

24 7 length of stay for all units and by unit; 3) to identify frequently used nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes, using NANDA International diagnoses, NIC interventions, and NOC outcomes respectively, on selected oncology units; 4) to identify the pattern of the linkages of NNN; and 5) to examine outcome change scores by different groups (e.g., age, gender, race, treatments, and type of insurance). Conceptual Model The structure-process-outcome framework by Donabedian (2003) was used as the framework for this study. Structure pertains to the conditions under which care is provided. It usually includes material resources, human resources and organizational characteristics. Process pertains to activities that constitute health care, including prevention, diagnoses, treatment, rehabilitation, and patient education. Outcomes pertains to desirable or undesirable changes in individuals and populations that can be attributed to health care. These outcomes would include changes in health status, knowledge, behavior, and satisfaction. The model is widely used in health care research and is well suited to describe nursing phenomena using NNN as the major concepts in the study. The structure elements include characteristics of patients and type of unit. The process elements encompass medical treatments chemotherapy, radiotherapy, surgery, and others), and nursing interventions. The outcomes elements mainly focus on nursing-sensitive patient outcomes measured by NOC. Other intermediate outcomes, such as length of stay (LOS), are included.

25 8 Figure 1 Donabedian's Model Structure Process Outcome Type of Unit Characteristics of Patients Disease Information Hospitalization Information Type of Insurance Medical Diagnoses Treatments Nursing Diagnoses Interventions Nursing- Sensitive Patient Outcomes Length of Stay Donabedian, A. (2003). An introduction to quality assurance in health care [R. Bashshur, Ed.]. New York: Oxford University Press. Research Questions The specific research questions addressed in the study are: Research Question One: What are the characteristics of the patients in the four oncology units of a large hospital? There are four categories of patient characteristics: Demographics (age, gender, race, marital status, years of education, and employment status), Disease information (site of tumor, duration of cancer since first diagnosed, stage of cancer, severity of illness, and risk of mortality), Hospitalization information (site of initial admission, oncology unit, length of stay, and discharge status), and a Cost-related variable (type of insurance).

26 9 Research Question Two: What is the length of stay for patients in each of the four oncology units? Does length of stay differ by unit? A there any differences in length of stay between age groups (over and under 65 years); medical treatment groups, such as patients receiving surgery procedures (SP), radiotherapy (RT), chemotherapy (CT), other treatments such as hormone therapy, or a combination of treatments above; type of insurance (Medicare, Medicaid, self-pay/uninsured, or private insurance)? Research Question Three: What nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes are frequently used by nurses on each of the four oncology units and overall? What is the relationship between groups of patients defined by the length of stay (< 1 day, 1 day LOS < 3 days, LOS 3 days) and nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes overall and at each of the four oncology units? Research Question Four: What are the most frequently used linkages of NANDA-I, NIC, and NOC in oncology? Research Question Five: What patient characteristics are associated with positive, no change, or negative nursing-sensitive patient outcome change scores at discharge of the respective patient s most common nursing diagnoses?

27 10 Definitions of Variables NANDA-I: A nursing diagnosis is defined as a clinical judgment about individual, family, or community experience/responses to actual or potential health problems/life processes. A nursing diagnosis provides the basis for selection of nursing interventions to achieve outcomes for which the nurse has accountability (Herdman, 2012, p. 515). NIC: An intervention is defined as any treatment, based upon clinical judgment and knowledge that a nurse performs to enhance patient/client outcomes (Bulechek, Butcher, & Dochterman, 2008, p. 3). NOC: A nursing-sensitive patient outcome is defined as an individual, family, or community state, behavior, or perception that is measured along a continuum in response to a nursing intervention(s). Each outcome has an associated group of indicators that are used to determine patient status in relation to the outcome (Moorhead, Johnson, Maas, & Swanson, 2008, p. 38). For this study, an outcome change score is defined as the difference between the first and last outcome scores recorded during hospitalization. This rating system replaces traditional dichotomized outcomes with a clear and measurable presentation of outcome, such as outcome change score of two outcome ratings scores using a five-point Likert scale as opposed to the goal was achieved or not achieved (Moorhead et al., 2008). Linkages of NNN NANDA-I, NOC, and NIC (NNN) are terminologies recognized by the ANA for use in practice. NNN provides useful tools to document planned and provided care in a

28 11 computerized system and facilitates communication between nurses and other health care providers (Johnson et al., 2012). Nursing-Sensitive Patient Outcomes Of great significance is that NOC provides nursing-sensitive patient outcomes and measures both positive and negative outcomes, to enable nurses to capture the response of patients to nursing interventions. NOC outcomes can be represented as quantified outcomes, differing from the traditional approach of using the goal achievement method as a binary variable of met or unmet, or yes or no. The five-point scale used in NOC provides continuity in monitoring changes in the patient outcomes, from admission to discharge or until the resolution of the problem. Most studies have focused on nursingsensitive patient outcomes that are negative, such as failure to rescue, falls, or other adverse events, except for the outcomes reflective of patient satisfaction with care. In contrast, the NOC includes many positive outcomes for individuals, families and communities, with only a few negative outcomes focused on adverse events (Moorhead et al., 2008). Today, few empirical studies include these three nursing components of practice (diagnoses, interventions and outcomes) directly retrieved from computerized nursing documentation systems. This study provides the researcher with a great opportunity to examine the relationships of nursing diagnosis, nursing interventions, and nursing outcomes in the computerized documentation system and to examine documentation completeness within the computerized system for a group of oncology patients. In addition, this study focused on the relationship between nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes at the individual level. This study

29 12 helps to define the knowledge base of oncology nursing care using SNTs (NNN) in an acute setting by identifying the most frequently used nursing diagnoses, outcomes, and interventions in four oncology specialty units. Planned Nursing Care Planned nursing care is an important decision of the role and function of nurses in the care process. It is not only a product of nursing professionalism, demonstrating critical thinking and decision-making, it is also a legal record of nursing care provided through documentation of the nurses assessments, treatments, and outcomes of care. The development of an effective documentation system is impeded by the separation of care planning from the documentation of care delivered within some EHRs. Ultimately nurses need a system that combines planned care for a patient with documentation of care delivered within one system using standardized nursing terminologies (nursing diagnoses, interventions, and outcomes). We also need to be able to capture the dose of an intervention. This includes the frequency of providing the intervention and the time needed to complete the activities associated with the intervention. In addition a graphing function of outcome scores in the system and calculation of change scores across the episode of care would be an important feature. Planned nursing care primarily consists of nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions. In this study, NANDA-I, NOC, and NIC were recorded in the electronic records of patients in the study to represent nursing diagnoses, nursing-sensitive patient outcomes, and nursing interventions, respectively.

30 13 Length of Stay (LOS) LOS is a commonly used indicator for patient quality care and cost. In the study, LOS is defined as hospitalization days between admission time and discharge time from one of the four oncology units. For patients admitted less than 24 hours will be reported by two decimals and use days as unit. NOC ratings and their change scores, and LOS are quality indicators for this study. Oncology Nursing Care Oncology is the study of cancer (American Cancer Society, 2010). The study focused on identifying and evaluating acute care oncology nursing care planning for cancer patients using nursing diagnoses, nursing interventions, and outcomes. To obtain the primary problems, interventions, and outcomes in oncology, nursing care allows researchers to evaluate oncology nursing care. Identifying patient characteristics also benefits nurses care delivery for specific needs of this population and provides a rationale for patient classification for appropriate oncology nursing care needs, based on other information such as the characteristics of nurse and units. For example, in the study of Cheung et al. (2011), younger patients with cancer experienced worse pain, whereas older patients reported poor appetite. A symptom cluster is defined as three or more concurrent symptoms that are related to one another. Age and gender impact symptom clusters experienced by patients with cancer. Breathlessness, fatigue, and anxiety are also distressing symptoms experienced by cancer patients (Chan, Richardson, & Richardson, 2011). Moreover, oncology patients symptomatic problems were their main concern. In this research patients with cancer often experienced multiple symptoms concurrently. Appendix A describes the study variables and their definitions.

31 14 Summary Due to increasing oncological cases and their tremendous medical cost, it is important to conduct research to identify nursing knowledge for current oncology patient care. Limited published studies using SNTs directly retrieved from EHRs in the general population are available, and few focus on a specific group, such as an oncology population. Subsequently, there are very few studies are available that identify core problems, nursing interventions, and nursing-sensitive patient outcomes for cancer patients. In addition, updated studies using SNTs in EHRs are required to identify current patient characteristics, planned nursing care, nursing sensitive patient outcomes, length of stay and other cost-related variables. The purposes of this study were to: 1). Describe patient characteristics on four oncology specialty units; 2). Examine length of stay (LOS) on four specialty units based on patient characteristics, such as demographics (gender, age), disease information (treatments and type of insurance); 3). Examine nursing process variables by identifying the frequently used nursing diagnoses, interventions, and outcomes for oncology patients in an acute care setting using standardized nursing terminologies (SNTs); 4). Examine common concurrent pattern of patient problems using nursing diagnoses (NANDA-I); and 5). Explore the relationship between outcome change scores and characteristics of patients. The next chapter focuses on a literature review of related publications and research that support this study.

32 15 CHAPTER II REVIEW OF THE LITERATURE Cost and Quality in Oncology Nursing Care The need for research focused on current oncology nursing care is increasing due to the complexity of conditions experienced by oncology patients, changes in demographics of cancer patients, and longer survivorship because of new treatments and advanced technology. This explains a growing demand for nurses specializing in oncology. In 2011, the American Cancer Society reported a total of 1,596,670 new cancer cases in the United States (U.S.) and 571,960 deaths from cancer are projected to occur (Siegel, Ward, Brawley, & Jemal, 2011). Currently, cancer accounts for one in four deaths in the U.S. In addition, it is estimated that one out of five individuals in the U.S. will develop cancer by Based on a growing and aging society, the National Institutes of Health projected that the overall cost of cancer was $124.6 billion in 2010 and would increase to approximately $158 billion by However, additional analysis, accounting for new technologies and treatments, increased the projected costs to 173 billion and considering a 5% annual cost increase, the projected cost was raised to $207 billion by 2020 (Siegel, et al., 2011). To lessen this burden, it is also critical to investigate current effective interventions and to relocate healthcare resource for appropriate oncology nursing care in order to optimize patient care, which will result in an expected reduction of unwanted medical waste of resources on ineffective treatments or interventions. Nurses have been the main workforce in oncology inpatient care. Nurses diagnose risk and problems based on patients status, intervene to relieve symptoms, and evaluate outcomes by applying

33 16 their professional knowledge and skill beyond the administration of medication or other medical treatments. New treatments and advance in technology have speeded up treatment time frames and changed the environment in health care delivery. It is imperative to conduct cost-effective research while reinvestigating change in demographics, and to evaluate current oncology nursing care by using nursing-sensitive patient outcomes and other commonly used healthcare outcomes, such as length of stay (LOS). With the use of SNTs in EHRs, nurse researchers can study planned nursing care and delivered nursing care in an efficient manner and enhances cost-effective research. Moreover, in an era of increasing EHRs, SNTs such as NANDA-I, NIC and NOC provide a means of collecting nursing data that are systematically analyzed within and across healthcare organizations and provide essential data for cost/benefit analysis and clinical audit (Herdman, 2012). Nursing Nomenclature Nursing nomenclature, nursing classifications, standardized nursing terminologies (SNTs), and standardized nursing languages (SNLs), are usually considered interchangeable concepts. These terms when used in an EHR aim to facilitate local, national, and international data sharing and comparison of nursing care provided to patients across settings. The terminologies/languages help communicate the focus of nursing care, support documenting nursing decisions, and increase shared decision documental making processes among healthcare providers, patients, and their families. Their use increases patient-centered healthcare of populations of patients across the care continuum because NANDA-I nursing diagnoses support documenting patients human response to disease; the NOC outcomes reflect the patients preferences for outcomes and

34 17 support collaboration or mutual setting of desired outcomes and the measurements of the outcomes; and the NIC interventions are performed by nurses with the involvements of patients and family. NANDA International, NOC, and NIC: The NNN Linkages A Brief Description of the NNN Linkages In 1973, the first Conference on the Classification of Nursing Diagnoses took place to classify health problems within the nursing domain and formed the North American Nursing Diagnosis Association (NANDA), now known as NANDA International (NANDA-I) (Herdman, 2012). Work on the Nursing Interventions Classification (NIC) (Bulechek et al., 2008), began in 1987, followed by the Nursing Outcomes Classification in 1991 (NOC) (Moorhead et al, 2008). Both classifications are developed and continually updated by the authors in the Center for Nursing Classification and Clinical Effectiveness (CNC) at University of Iowa. NANDA-I, NIC, and NOC (NNN) are three of the thirteen recognized American Nursing Association (ANA) recognized terminologies (Dochterman & Jones, 2003; Rutherford, 2008). NANDA International The NANDA-I Taxonomy II ( ) has 13 domains, 47 classes, and 216 labels for nursing diagnoses. Each diagnosis comprises a label or name for the diagnosis, a definition, defining characteristics, risk factors or related factors. It also provides core references and levels of evidence for each diagnosis (Appendix B). Accurate and valid nursing diagnoses guide the selection of interventions that are likely to produce the desired treatment effects and determine nurse-sensitive outcomes. Nursing diagnoses are

35 18 keys to the future of evidence-based knowledge, professionally-led nursing care and to more effectively meeting the need of patients (Herdman, 2012). Nursing Interventions Classification The taxonomy of NIC (5th ed.) has seven domains, 30 classes, and 542 labels for nursing interventions. The seven domains are: Physiological: Basic, Physiological: Complex, Behavioral, Safety, Family, Health System, and Community. Each intervention comprises a label or name for the intervention, a definition, and a list of activities. It also provides background readings for each intervention (Appendix C). The Nursing Interventions Classification (NIC) is a comprehensive, research-based, standardized classification of interventions that nurses perform (Bulechek et al., 2008, p. 3). Nursing Outcomes Classification The NOC Taxonomy (4th ed.) has seven domains, 31 classes, and 385 labels for nursing-sensitive patient outcomes. Each outcome has a label or name for the outcome, a definition, a list of indicators that can be used to evaluate patient status in relation to the outcome, a target outcome rating, a place to identify the source of data, a five-point Likert scale to measure patient status, and a short list of references (Appendix D). The Nursing Outcomes Classification (NOC) is a comprehensive, standardized classification of patient/client outcomes developed to evaluate the effects of nursing interventions (Moorhead et al., 2008, p. 26). The NNN Linkages The nursing classifications NANDA-I, NIC, and NOC are language systems designed to represent three interacting and cyclical elements of nursing care: diagnoses, interventions, and outcomes (Dochterman & Jones, 2003). Nursing diagnoses describe

36 19 actual, potential and health promotion needs. Nursing interventions are nursing actions that assistant a patient through a process to a desired outcome. Unlike nursing diagnoses or patient outcomes whose core concern is for the patients, the focus of nursing interventions is on nursing behavior. Patient outcomes serve as the criteria against which to judge the success of a nursing intervention. The outcomes have been developed to be used in all settings, all specialties, and across the care continuum. The linkages between these SNTs facilitates the use of SNTs in practice, education and research (Johnson et al., 2012). The third edition of the book focused on NNN linkages, NOC and NIC linkages to NANDA-I and Clinical Conditions: Supporting Critical Reasoning and Quality Care, was released in 2011 (Johnson et al., 2012). In this book, the relationships between the NNN linkages are described from aggregated data and expert opinion to assist nurses and students with clinical decision-making. The links between the NANDA-I diagnoses and the NIC (5th ed.) interventions suggest the relationship between a patient s problem and the nursing actions that will resolve or diminish the problem or risk. The relationship between the NANDA-I diagnoses and NOC (4th ed.) outcomes suggest the relationships between a patient s problem or risk and health care status and those aspects of the problem or status that are resolved or improved as expected by one or more interventions. The links between the NOC outcomes and the NIC interventions suggest a similar relationship focused on the resolution of a problem and the nursing actions directed at a problem resolution. This means the outcome is expected to be influenced by the interventions provided by the nurse. The descriptive relationships assist nurses in

37 20 defining and gaining nursing knowledge using SNTs, and enhancing their clinical judgment by providing a good resource to improve their critical thinking skills. Three Theory Explanations of the NNN Linkage Margaret Lunney provided theoretical explanations for the NNN linkages from three different perspectives: Hayakawa s linguistics theory, critical thinking perspectives, and the concept of accuracy of nurses diagnoses and thereby enhances their competence in the nursing process. Linguistics theory explains that naming a scientific phenomenon is important for knowing about that phenomenon. Use of the SNL facilitates improvements in nursing care by fostering collaboration and cooperation among nurses, consumers, and other providers. Critical thinking processes and outcomes are also enhanced by nurses: 1. Identifying and challenging assumptions central to critical thinking. 2. Challenging the importance of context to critical thinking. 3. Imagining and exploring alternatives. 4. Imagining and exploring alternative leads to reflective skepticism (Dochterman & Jones, 2003). Critical thinking leads to accurate interpretation of clues in a clinical situation in order to identify patient problems, risk states, or readiness for health promotion. Many studies of the NNN linkages strengthen the ability of critical thinking by applying the Outcome Present state Test Model (OPT model) are available in the literature (Kautz et al., 2009; Kautz, Kuiper, Pesut, & Williams, 2006; Kautz, Kuiper, Pesut, Knight-Brown, & Daneker, 2005; Kautz & van Horn, 2008; Kuiper, Kautz, & Williams, 2005; Kuiper, Kautz, & Pesut, 2004). Accuracy of interpreting the cues in a clinical sitiation in order to

38 21 identify clinical problems, risk factors, or health promotion is an outcome of critical thinking (Dochterman & Jones, 2003). Accuracy of diagnoses is important because it guides the selection of interventions and outcomes. Without critical reasoning, nurses are unable to select appropriate interventions and outcomes linking to the nursing diagnoses. Therefore, developing the ability to think critically is the essence of learning nursing knowledge across academic nursing programs. However, awareness of the need of SNTs has not been the focus of all nursing academic programs in spite of the inclusion in most text books. More studies are required to provide evidence of the benefits in using SNTs in EHRs in order to alter the current academic environment and increase their use in practice. The harmonization of NANDA-I, NIC and NOC facilitates the use of each SNT and identification of the most appropriate categories of diagnoses, interventions and outcomes. Standardized Nursing Terminologies in EHRs Meaningful Use for the Nursing Profession SNTs are required for a successful innovation of EHRs in the nursing profession (Jha, 2010). SNTs in EHRs may potentially address Meaningful Use (MU) for the nursing profession by linking quality measures within the EHRs, such as NOC outcome ratings to their relevant NNN linkage, and thereby demonstrating the change in outcome scores that were improved by nursing interventions. The term MU comes from the Medicare and Medicaid EHR Incentive Programs, which provides a financial incentive for the meaningful use of certified EHR technology to achieve health and efficiency goals. To demonstrate MU for the financial incentives, it requires a report of measures related to quality improvement through EHRs. Nursing-sensitive patient outcomes, as

39 22 quality measures, should also be emphasized in order to visualize nurses independent contribution to patient quality care. These nursing-sensitive patient outcomes have to be viewed as quality measures not only by the nursing profession but also by other healthcare disciplines. SNTs in EHRs can provide a way to demonstrate Meaningful Use (MU) from a nursing perspective, but has not been included in the financial incentive at the current time. Nursing s Contribution to Quality Patient Care Nurses face two challenges: First, it is difficult to differentiate the contributions of nurses to the quality of patient care from other health providers. Secondly, it is a challenge to incorporate standardized terminologies of nursing care into Electronic Health Records (EHRs) without sacrificing individualized patient-centered care (Rutherford, 2008). The use of SNTs within EHRs is the key to capturing the independent contributions of nurses and differentiate the uniqueness nursing care provides from other disciplines. For example, NANDA-I (Herdman, 2012), NOC (Moorhead et al, 2008), and NIC (Bulechek et al., 2008), recognized terminologies by ANA, were developed for use in practice and research and have demonstrated their use in different studies (Head et al., 2011; Lee, Park, Nam, & Whyte, 2011; Müller-Staub, Lavin, Needham, & van Achterberg, 2007; Müller-Staub, Needham, Odenbreit, Lavin, & van Achterberg, 2008; Müller-Staub & Paans, 2011; Müller Staub, 2009; Müller Staub, Lavin, Needham, & van Achterberg, 2006; Müller Staub et al., 2008; Müller Staub Lunney, Odenbreit, Needham, Lavin,, & van Achterberg., 2009; Müller Staub, Needham, Odenbreit, Lavin, & van Achterberg, 2007; Thoroddsen, Ehnfors, & Ehrenberg, 2010).

40 23 Additionally, NANDA-I represents nursing diagnoses, NIC encompasses nursing interventions, and NOC encompasses patient outcomes. Therefore, the NNN linkages provide useful tools to document planned care in computerized systems, to evaluate the care provided, and facilitate communication between nurses and other health care providers. Importantly, NOC outcomes capture the response of the patient to nursing interventions to evaluate care with patients progression or decline. Rather than a traditional approach of outcome met or unmet, outcome ratings with a five-likert point scale can be used to monitor changes in patient outcomes from admission to discharge or to the resolution of a problem (Moorhead et al., 2008). The magnitude of change scores from outcome ratings presents a patient s status and it is important for nurses to notice these changes in real time situation, then altering care interventions over time to improve patient outcomes. Benefits of SNTs in EHRs Menachemi and Collum (2011) reported advantages of EHRs in three categories: clinical, organizational, and societal outcomes. First, EHRs improve clinical outcomes by providing guided standardized care for decision-making, so that it leads providers to an increased level of adherence to evidence-based clinical practices. Secondly, researchers can focus on overall cost reduction by timely, accurate charting in a paperless world from an organizational perspective. Finally, the societal benefit relates to advantages resulting from the broad implementation of EHRs and shared patient information, thus decreasing duplication of information in the Health Information Exchange (HIE) resources. HIE is the act of transferring health information electronically between two or more entities. For example, EHR warehouses provide an improved ability to conduct research and also

41 24 enable individuals to aggregate data to be aggregated across settings or populations in order to contribute to the needs of the society (Menachemi & Collum, 2011). Specifically, well-developed SNTs within EHRs benefit by providing: 1) better communication among nurses and other healthcare providers (Lundberg et al., 2008; Rutherford, 2008), 2) increased visibility of the nursing care process (Muller-Staub et al., 2008; Rutherford, 2008), such as nursing diagnoses, nursing interventions, and nursingsensitive patient outcomes, 3) increased adherence to standards of care, which may promote evidence-based practice care as well (Bruner, Corbett, Gates, & Dupler, 2012; Kautz & van Horn, 2008; Rutherford, 2008), 4) facilitation of evaluation of nursing competencies (Rutherford, 2008), 5) increased quality improvement, patient satisfaction, shorter LOSs, and cost reduction (Jansson, Pilhammar-Andersson, & Forsberg, 2010; Muller-Staub, Lavin, Needham, & van Achterberg, 2006; Saranto & Kinnunen, 2009), and 6) increased legibility and accessibility of data (Holroyd-Leduc, Lorenzetti, Straus, Sykes, & Quan, 2011). Effective Communication and Quality of Documentation SNTs support effective communication in terms of the presentation of patient problems, interventions, and outcome changes (Lundberg et al., 2008). However, inconsistent documentation challenges researchers to interpret and understand the results of nursing care (Neiman, Rannie, Thrasher, Terry, & Kahn, 2011). Studies of SNTs in EHRs reported inconsistent findings, in such factors as time for documentation (Vizoso, Lyskawa, & Couey, 2008); but in another review, the documentation time was shorter after allowing for an expected learning curve (Holroyd-Leduc et al., 2011). Therefore, it

42 25 is necessary to conduct research on any inconsistent finding generated and then guide the clinical practice for improvement of patient care. Comprehensive Planned Care SNTs not only enhance clinical thinking (Dochterman & Jones, 2003) but also promote a better plan of care. In a study of SNTs by Müller-Staub and her colleagues, the use of SNTs promoted more comprehensive nursing care plans. This not only led to more visible nursing care in the nursing process documentation, but also resulted in more reliable nursing data available to impact personnel and budget planning (Müller-Staub, Needham, Odenbreit, Lavin, & van Achterberg, 2007). In addition, the use of NANDA-I nursing diagnoses was found to improve the quality of documented patient assessments across the domain of nursing practice (Muller-Staub, Lavin, Needham, & van Achterberg, 2006, 2007). The NNN linkages assist nurses in decision-making for a better patient care (Johnson et al., 2012). NANDA-I diagnoses supported the holistic patient care by using an assessment framework, which guides to support nurses in comprehensively care planning (Herdman, 2012). Moreover, NOC outcome ratings provide measurable indicators for nurses to evaluate their patient care. The function of NOC ratings enables nurses to easily monitor the changes of patients health status using a one-to-five point Likert scale. Evidence-Based Practice (EBP) Bruner and colleagues analyzed the concept of clinical significance (CS) in relation to evidence-based practice (EBP) and concluded that a standardization of terminology is essential to disseminating best practices in the nursing profession (Bruner et al., 2012). A study of the NNN linkages to enhance EBP was published (Kautz & van

43 26 Horn, 2008). The Hartford Center at University of Iowa has developed evidenced-based guidelines incorporating to NIC and NOC. These evidence-based protocols provide nurses with good resources for research, education, and practice. Quality Improvement, Patient Satisfaction, Cost Reduction and a Shorter LOS Jansson et al (2010) reported planned nursing care may impact patient satisfaction by providing individualized care, participation, and shorter lengths of stay. Saranto and Kinnunen (2009) also found in reviewing 41 studies that structured standardized terminologies may improve the quality of patient care and shorten patients length of stay in 41 of their reviewed studies from 2000 to Use of SNTs can facilitate the measurement of nursing s contribution to care and thereby for a potential benefits in relocating the charge of nursing care in an appropriate categories instead of room fee (Muller-Staub et al., 2006). Legibility and Accessibility Holroyd-Leduc et al (2011) reviewed 30 articles related to EHR use in a structureprocess-outcomes framework and concluded that legibility and accessibility is the only clear advantage that EHRs have over traditional paper-based records. The findings focused on the advantages of EHRs usually did not report massive improvements in healthcare due to design limitations. Low Adoption Rate of SNTs in EHRs Just as standardized terminologies within EHRs for physicians have been commonly used with the application of ICD-10 and diagnostic related groups (DRGs), SNTs have been emphasized by nursing informatics specialists as a required element

44 27 within in the data to describe care provided by nurses. Yet, a lack of consensus by the nursing profession on the importance of including SNTs in EHRs impedes the development and implementation of a comprehensive nursing information system for documentation. This has resulted in a low adoption rate in practice, lack of awareness of the importance for nursing research, and minimal focus of nursing informatics content in curriculum design across different specialties in nursing. A recent survey of 1,268 nurses reported a low adoption rate of SNTs in clinical practice and academic settings (Schwiran & Thede, 2011). Specifically, 10.8% of nurses charted with NANDA-I, 7.5% with NIC, 6.9% with NOC, and less than 5% with remaining SNTs. In this sample, more than one third of students (39%) were enrolled in master s programs (n=219, students in total) (Schwiran & Thede, 2011). Christopher et al., reviewed 50 charts and found only 28% of the charts used a standardized care plan (Christopher, Flood, Carlson, Delaney, & Krch- Cole, 2011). This report identified the demand for education related to SNTs in clinical settings and academic curriculums. In addition, funding for EHRs that supports nursing process content is needed to ensure that the care nurses provide is part of the EHR in the U.S. It also needs to be mentioned, there is a critical lack of funds available to support use of SNTs in nursing in contrast to Medicine. Nursing Research Focused on Health Systems, Technology, and Nursing Specialties The perspective of health systems and organizational policy cannot be ignored because they influence study results. One particular nursing policy is that the plan of care has to be initiated within 24 hours after admission. This policy can impact the amount and quality of data available for very short lengths of stays. The scope of Information

45 28 system applications implemented in the organization are another factor that might alter the findings or data available to describe the care provided to patients (Brender, 2006; Friedman & Wyatt, 2006). For example, an underdeveloped information system may cause duplicative documentation by the nurse for the same patient and without warning messages, thus duplicative information could vary from prior entry. Additionally, after decades of development of nursing specialties, nurses in each specialty focuses on the development in their own specific knowledge, rather than involving a broader scope of practice that may influence their practice. For example, few nursing oncology studies discuss the impact from technology and directions of future studies. A broader focus across each specialty is necessary to examine any emerging issues that are beyond the scope of specialty practice, such as the impact of technology. These new emerging issues, such as health systems, technology, and language classifications, have to be discussed for each specialty. Ehealth or enursing is an emerging term, describing the discipline of technology in the health care delivery system and in the nursing profession. There is a need for this emerging specialist of nursing informatics and these individuals should be involved in the development of a nursing information system. In practice, underdeveloped nursing documentation systems have impeded the innovation of enursing and have become common issues in all clinical settings in the U.S. One potential reason is that organizations have insufficient knowledge of the essential elements of nursing information systems and the characteristics of what represents an effective nursing documentation tool in current health care systems. Moreover, without SNTs, an effective nursing documentation tool will never exist. Current underdeveloped nursing information

46 29 systems have been an obstacle for efficiently documenting nursing care in each specialty. Few grants have been announced to support this type of study. The lack of support further delays development of enursing applications to support practice. Few nursing studies illustrate organizational factors or system-wise variables in their findings by using SNTs. Aiken and colleagues work on systematic variables such as staffing or skill mix in health care did not emphasized SNTs in their findings. However, organizational information is useful in providing accurate interpretation of findings in research because more and more studies support that system factors have to be concerned when studies occurs in an organization, for example, the policy of completion of nursing diagnosis in the care plan within 24 hour admission. Donabedian s structural process outcome model has provided a commonly used framework in healthcare and a macro-view perspective as well for health science research. In summary, nursing researchers have to use SNTs as well as other concerns, such as health systems or context of care, and technology in their study interests, in order to advance the nursing profession and influence and bring about health policy change in the era of enursing. Oncology Nursing Care Nurses working in oncology care for patients with a variety of problems and symptoms that impact quality of life and then require nursing interventions. Cancer Symptom and Symptom Cluster The terms of symptom, symptom experience, symptom prevalence, symptom severity, symptom intensity, and symptom distress are frequently studied in oncology care (Badger, Segrin, & Meek, 2011; Gilbertson-White, Aouizerat, Jahan, & Miaskowski,

47 ). Pain and most other symptoms are treatable for patients with cancer and patients often concurrently experience multiple symptoms (Karabulu, Erci, Özer, & Özdemir, 2010; Kirkova, Walsh, Aktas, & Davis, 2010). Measuring symptom burden is required for these patients (Cleeland & Reyes-Gibby, 2002). Gilbertson and colleagues (2011) reviewed 22 studies focused on palliative care of patients with advanced cancer and found pain, dyspnea, and nausea were all identified as important symptoms for these patients. Other symptoms evaluated in at least 50% of the studies reviewed were depression, constipation, anorexia, sleep disturbance, anxiety, vomiting, fatigue, weight loss, cough, dysphagia, and drowsiness. Due to the diversity of measures and approaches used in the 22 studies reviewed, the researcher could not identify a reliable summary of the prevalence of symptoms in cancer patients. Karabulu and colleagues (2010) reported that most symptoms were fatigue, difficulty remembering, sadness, poor appetite, lack of enjoyment of life, pain, distress, difficulty walking, and dry mouth. The least experienced symptoms were shortness of breath and vomiting. In general, 37.5% of the patients experienced moderate symptoms and 12.5% experienced severe symptoms. Since the shift to symptom clusters has occurred, clinical and research foci has changed to clusters to the experience of the whole human being rather than one specific symptom. This shift aids in identifying the most effective interventions for patient care (Matthews, Schmiege, Cook, & Sousa, 2012). For example, pain, depression, and fatigue were reported as an identifiable symptom cluster and associated with declining physical functioning (Laird et al., 2011; Thornton, Andersen, & Blakely, 2010; Torta & Munari, 2010). A symptom cluster is defined as

48 31 three or more concurrent symptoms that are related to one another (Kirkova et al., 2010; Xiao, 2010). Characteristics of Patients and Oncology Nursing Care Studies reported that the demographics of patients, disease, and individual characteristics were associated with symptom/symptom clusters (Matthews et al., 2012). Few studies in nursing have addressed the association between demographics or other variables and symptom clusters. Cheung and colleagues (2011) found that younger patients with cancer experienced worse pain, whereas older patients reported poor appetite (Cheung, Le, Gagliese, & Zimmermann, 2011). Matthews et al., (2012) reported demographic characteristics, individual characteristics and mood were significantly associated with the three symptom clusters of pain-insomnia-fatigue, cognitive disturbance-outlook, and gastrointestinal problems. Karabulu et al. (2010) also reported demographics and disease information had influence on experience of symptom of the disease. The findings of this study suggest more studies on the relationship between demographics and symptom clusters are needed to explore evidence-based interventions useful to treat these symptom clusters. Psycho-education interventions have been found to be effective for patients with cancer suffering from breathlessness, fatigue, and anxiety (Chan et al., 2011). Moreover, symptom clusters were also associated with patient distress and poor outcomes, such as functional status, quality of life, and mood (Matthews et al., 2012). NOC and Measurement of Symptom Clusters The importance for symptom cluster evaluation in oncology has been highlighted (Esper, 2010). Few nursing studies focus on symptom clusters due to a lack of daily used

49 32 measures of symptom severity and intensity in nursing documentation. Use a questionnaire of symptom intensity that is separated from nursing documentation will increase the burden of data collection. A standardized profile of symptoms to enable nurses to focus on effective approaches to alleviate symptom clusters is required. Health providers should also evaluate the frequency and severity of symptoms (Karabulu et al., 2010). Therefore, a standardized scale of nursing-sensitive nursing outcomes, such as Nursing Outcomes Classification (NOC) scores, will provide a potential tool to evaluate symptom clusters, including symptom frequency and symptom intensity across populations and settings. Kirkova and colleagues (2006) reviewed 21 studies and concluded that an ideal symptom assessment instrument should capture symptom prevalence, severity, distress, and assess symptom clusters. In the area of validity and reliability the instrument should be precise and stable among raters over time. In terms of measurement, the scale used in the instrument should be easy to understand, complete, have clinical utility, capture changes over time, and commensurate for statistical analysis. The use of the instrument should have minimal burden for the patient and staff and provide enough information for decision-making. Finally, it should have a tool for evaluation purposes. Based on Kirkova s criteria for a good tool for measurement, a study using SNTs, specifically NOC, is required to evaluate the ability of SNTs to measure symptom severity in cancer patients. Cost-Effectiveness Research SNTs are expected to benefit the care process so research in cost-effectiveness and a study of SNTs may potentially provide staffing and budget planning information

50 33 (Müller-Staub et al., 2007). It is critical to conduct studies focusing on nursing care process using SNTs in EHRs, although EHRs have been widely studied to improve quality of care and reducing cost by saving nursing time and shortening the length of stay (Hillestad et al., 2005). LOS in Oncology Units Length of stay (LOS) is a common outcome in research studies and is associated with health care cost (Gupta, Vashi, Lammersfeld, & Braun, 2011; Laky, Janda, Kondalsamy-Chennakesavan, Cleghorn, & Obermair, 2010; Youngwerth, Bartley, Yamashita, & Kutner, 2011). It also serves as a proxy quality indicator. For example, correcting malnutrition is recommended before surgery and serves as a better predictor for LOS (Gupta et al., 2011). Laky et al. (2010) found malnutrition, low quality of life scores, and advanced cancer in gynecological cancer patients to be associated with prolonged lengths of stay (Laky et al., 2010). Fogh and colleagues found elderly people undergoing induction of radiotherapy before resection for treatment of esophageal cancer had no prolonged LOS compared to younger patients (Fogh et al., 2011). Patients on an oncology acute care unit for elders require nutritional consults 2.1 times more often and are 2.5 times more likely to have nutrition supplements ordered than usual care cancer units (Flood, Brown, Carroll, & Locher, 2011). Patients with malnutrition status had prolonged LOS, and were likely to be readmitted within 15 days, and suffered increased mortality (Lim et al., 2011). In a study, oncology patients had a LOS median of 7 days, with a range from two to 20 days and 12 % of patients died while in the hospital (Mansour, Simcock, & Gilbert, 2011)

51 34 Defining Nursing Knowledge In a cross-sectional review of nursing records (n=265) in four specialties, SNTs, particularly, NNN, were capable of distinguishing between specialties and defining knowledge for each specialty. It also reported that the most frequent nursing diagnoses focused on basic human needs of patients across specialties. Further studies with large data sets are required to explore the relationships between nursing diagnoses and nursing interventions in order to make explicit the knowledge in each nursing specialty (Thoroddsen et al., 2010). NANDA-I, NIC, and NOC in Oncology Nursing Care Studies of oncology nursing care using SNTs were mostly focused on nursing diagnoses and were published in the 1990s in journals mostly relevant to SNTs (Courtens & Abu-Saad, 1998; Tiesinga, Dassen, & Halfens, 1996). In general journals, use of SNTs is seldom indicated. Most recent studies of SNTs occurred outside the United States (U.S). In a Delphi study, Speksnijder and colleagues identified 64 NANDA-I diagnoses relevant to hematology oncology in Europe. Experts agreed on the importance of these 11 diagnoses: Imbalanced Nutrition: Less Than Body Requirements, Diarrhea, Fatigue, Risk for Bleeding, Risk for Infection, Impaired Oral Mucous Membrane, Risk for Impaired Skin Integrity, Impaired Skin Integrity, Hyperthermia, Nausea, Acute Pain, and the health problem Pruritis (Speksnijder, Mank, & van Achterberg, 2011). Courtens and Abu-Saad studied 15 nursing records of leukemia patients. The subjects were73% female with an average age of 46 years (range of age from 24 to 69). Their length of stay (LOS) in the hospital averaged 42 days. In relation to the number of diagnoses, there were 16 per patient with a range of diagnoses from 1 to 24. Yet, the

52 35 researchers identified 36 different diagnoses classified into nine functional health patterns (Courtens & Abu-Saad, 1998) (Appendix E). In the study of Cpirtens and Abu-Saad, the most frequent diagnoses were Sleep Disturbance, Risk For Bleeding, Fluid Volume Excess, Skin Problems, Fatigue, Pain, Altered Nutrition: Less Than Body Requirements, Nausea, Altered Oral Mucous Membrane, Risk For Infection, Diarrhea, and Self-Care Deficit related to hygienic care (67% of patients). The problem of pruritus, nausea, vomiting, tingling, and dizziness were not listed as NANDA-I diagnoses. Chronic Pain, Risk for Infection, and Activity Intolerance were reported as the most frequent used nursing diagnoses (n=539) in a study of patients with breast cancer in Japan (Ogasawara et al., 2005). Anxiety was reported to be experienced by patients in varied levels and required assessment at different points during care for patients undergoing Bone Marrow Transplant (Young, Polzin, Todd, & Simuncak, 2002). The NOC outcome Anxiety Level is able to meet this requirement for consistent monitoring. Disturbed Body Image and Grieving were described in a study of chemotherapy-induced alopecia/hair loss. The study recommended that relevant nursing interventions (NIC) and nursing-sensitive patient outcomes (NOC) should be identified and developed to address these problems (Dougherty, 2007). Summary Cancer burden is a well-known problem in the United States. Nurses are the main health care workforce that provides cost-effective oncology care using SNTs within EHRs. Studies found that SNTs within EHRs benefit the care process by providing comprehensive planned care, better decision-making, evidence-based practice, increased patient satisfaction, quality improvement, and a shorter LOS, reducing cost and time in

53 36 repetitive documentation. However, only a few studies using SNTs focused on oncology nursing care in the U.S. SNTs may potentially benefit future research exploring symptom clusters using NNN. NANDA-I diagnoses, NOC outcomes, and NIC interventions could be used to describe the symptom clusters experienced by cancer patients. Moreover, NOC and change outcome ratings can provide consistent monitoring of symptoms that may fluctuate by stage of cancer or type of treatment. SNTs within EHRs may also provide further insight toward reducing the burden of cancer. In conclusion, SNTs, such as NNN, provide a beneficial tool in advancing nursing knowledge in oncology and promoting quality patient care.

54 37 CHAPTER III RESEARCH DESIGN, METHODS, AND DATA ANALYSIS Research Design This research used a descriptive retrospective design to identify demographic characteristics and planned care of cancer patients on four oncology specialty units. The descriptive design was selected to capture current demographics in oncology acute care and undocumented nursing planned care using SNTs in EHRs (Brink & Wood, 1998). Descriptive research designs are used to describe "what exists" with respect to variables or conditions in a situation. A retrospective study looks backward for a relationship between selected phenomenon that may have occurred in the past (Houser, 2012). In addition, descriptive studies can make two distinct contributions to nursing research. Complete descriptions of specific phenomena within a population are critical to theory building (inductive) as well as to theory testing (deductive) (Brink & Wood, 1998). In other words, the data collected will either contribute to the development of theory or explain phenomena from the perspective of the population being studied (Houser, 2012). Without the interference of an investigator, descriptive studies always examine the variables or sample as they currently exist (Brink & Wood, 1998; Houser, 2012). The ideal sample for a descriptive design is either the total available population (particularly with small populations) or a randomized sample from a target population (Brink & Wood, 1998). This study selected first admission for each patient during the study period for the puspose of maintaining a sufficient sample size and independence of the sample. This was important because not all patients had a plan of care and some individuals had mutiple admissions. Randomization may cause reduction in the sample

55 38 size and questionable issues on independence of the sample may result in bias of the findings. Data presentation in this type of design is usually in the form of descriptive statistics (such as frequencies) and occasionally correlations or differences between groups may be presented but this does not alter the type of design (Brink & Wood, 1998). The use of available data, one of the methods of data collection for descriptive designs was applied in this study. It suggests that research with a descriptive design use available data and requires that the investigator check data carefully to confirm data quality from the resources used in the research (Brink & Wood, 1998). Setting and Sample The data were retrieved from the records of patients discharged from four oncology units in a 762-bed Midwest tertiary care hospital from June 1, 2010 through December 31, The start date for data extraction was selected one year after an HER update to the computerized nursing documentation system, which was NANDA-I, NOC, and NIC (NNN), implemented in May, NNN is used in the electronic nursing documentation system of this hospital that uses an Epic software application. The four units selected for the study were Medical-Surgical Oncology Unit (Unit M) with 24 beds, Hematology Oncology Unit (Unit H) with 19 beds, including 4 beds designed for Hospice care, Gynecology Oncology Unit (Unit G) with 21 beds, and Adult Leukemia and Bone Marrow Transplant Unit (Unit A) with 26 beds. To be included in the study during the time period, patients had to be discharged from the four oncology units from June 1, 2010 through December 31, Pediatric patients were excluded because this is a study focusing on adult oncology patients.

56 39 Variables and Measures This study included the structural variables (characteristics of the patient population and disease information including severity of illness, and risk of mortality), the process variables (medical diagnosis, planned medical treatments, nursing diagnosis, and planned nursing interventions), and the outcome variables (length of stay, nursingsensitive patient outcomes, and change scores using NOC). In addition, datum related to type of insurance was collected. The definitions of the variables used in this study can be found in Appendix A and includes two parts. The first part includes characteristics of patients: demographics (age, gender, race, marital status, years of education, and employment status), disease information (medical diagnosis, site of tumor, duration since first cancer diagnosed, stage of cancer, severity of illness, and risk of mortality), hospitalization information (initial admission site, discharge unit, admission dates, discharge date, and length of stay), and one cost-related variable (type of insurance). The researcher retrieved demographics and cancer information directly from electronic health records (EHRs) and the local Tumor Registry. The second part includes SNTs (NANDA- I, NIC, and NOC) and the start dates and end dates of care, and NOC outcome ratings. The SNTs, NANDA-I as nursing diagnosis, NIC as nursing interventions, and NOC as nursing-sensitive patient outcomes, are coded in multiple levels in plan of care documentation in EPIC. A sample of the NANDA-I nursing diagnosis, Acute Pain, is presented in Appendix B. Although this is a retrospective descriptive design, performed medical treatments and planned nursing interventions were used as process indicators. Medical treatments were surgery procedures related to cancer (SP), chemotherapy (CT), radiotherapy (RT)

57 40 and other types of treatments. Nursing intervention labels (NIC) were collected from plan of care documentation in EPIC. A sample of the NIC intervention, Pain Management, is presented in Appendix C. Outcomes variables include NOC outcomes and their outcome rating, and length of stay (LOS). NOC outcome ratings and the date assessed were collected from plan of care documentation in Epic. The ratings at admission and at discharge were calculated as the outcome change scores. The change scores based on the most common linkages of NNN was applied to answer Research Question Five. A sample of the NOC outcome, Pain Level, is presented in Appendix D. Besides NOC outcome change scores, length of stay (LOS) was used as another outcome in Research Question Two. To obtain accurate LOS and age, SAS function INTCK is basically used to get the number of time intervals between two dates. Time intervals were specified in minutes for LOS and in month for age. The date or time of admission to oncology unit was used to obtain LOS and age. Two decimal was used to present the unit of day for LOS. To be specific and prevent from a zero value for LOS, admission time to oncology unit was used (data collected from the hospital was coded as MM/DD/YYYY XX:XX). The SAS codes were: LOS = round ((INTCK ( minute, admission time to oncology unit, discharge time))/1440, 0.01); Age = floor (INTCK ( month, date of birth, admission date to oncology unit) (day (admission date to oncology unit) < day (date of birth)))/12) (Cody, R. P., & SAS Institute, 2010). Data Collection and Management Inclusion criteria are: all records of patients who were discharged from the four oncology units Gynecology, Oral Surgery, and Otolaryngology (Unit G), Medical

58 41 Surgical Oncology Unit (Unit M), Hematology/Oncology and Palliative Care Unit (Unit H), and Adult Leukemia and Bone Marrow Transplant Unit (Unit A) during June1 st, 2010 through December 31 st, 2010 were included in the study. Adult patients that fit the inclusion requirements were included in the study. Patient identifications in the hospital data base were scrambled and a unique identifier was created by the researcher for the purpose of patient confidentiality. The completeness and accuracy of the data was randomly checked on each 100 th file to ensure the data quality. Data collection was completed in one year. Data management and data preparation for analysis was completed by the researcher. Several strategies were performed to confirm the data quality. First, for data from electronic medical records, (e.g., age, gender, LOS) were confirmed with another resource, the Tumor Registry. Secondly, the variables for planned care (e.g., NANDA-I diagnoses, NIC interventions, and NOC outcomes) were confirmed for accuracy by three strategies: use function of data validation in EXCEL as the initial entry, randomly data recheck per 100 records during data entry, and use of the data filter function in SAS Enterprise Guide 4.3 with SAS 9.3 after the completion of data collection (Davis, 2007). Data Analysis and Interpretation The data were analyzed using Enterprise Guide 4.3 with SAS 9.3 (Davis, 2007). Data analysis was performed according to each research question. Research Question One: What are the characteristics of the patients in the four oncology units of a large hospital? There are four categories of patient characteristics:

59 42 Demographics (age, gender, race, marital status, years of education, and employment status), Disease information (site of tumor, duration of cancer since first diagnosed, stage of cancer, severity of illness, and risk of mortality), Hospitalization information (site of initial admission, oncology unit, length of stay, and discharge status), and a Cost-related variable (type of insurance). Demographics, disease information, hospitalization information, and cost-related variables are described. Two collected variables, site of initial admission and Medicare Severity-Diagnosis Related Group (MS-DRG), are not described in the study due to their variability and difficulties in grouping them into categories for analysis. To answer Question One, a descriptive analysis were reported by using (a) frequencies and percentages for categorical variables, and (b) means, standard deviation, and ranges for continuing variables, in each unit and for the overall sample in the oncology population. Research Question Two: What is the length of stay for patients in each of the four oncology units? Does length of stay differ by units compared to overall length of stay for the other three units? At the unit level and overall, are there any differences in length of stay between age groups (over and under 65 years); medical treatment groups, such as patients receiving surgery procedures (SP), radiotherapy (RT), chemotherapy (CT), other

60 43 treatments such as hormone therapy, or a combination of treatments above; type of insurance (Medicare, Medicaid, self-pay/uninsured, or private insurance)? To answer Question Two, frequencies of nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes were analyzed and percentages were presented within and across units. Means, standard deviations, and ranges of length of stay in the oncology population were analyzed, within and across units for age group, treatment group and type of insurance. Normality tests were performed first. Parametric methods, such as ANOVA, were preferred for the study. Data transformation was performed since the dependent variable, LOS, violates the assumption of normality. A significant violation of the assumption of normality can seriously increase the chances of the researcher committing either a Type I (overestimation) or Type II (underestimation) error, depending on the nature of the analysis and the non-normality. Data transformations are the application of a mathematical modification to the values of a variable. Logarithmic transformations are common procedure for positive skewed data such as LOS (Moran & Solomon, 2012). Non-parametric methods, such as Kruskal- Wallis, may be selected only when normality is severely violated for the sample. Since the sample size is sufficient to perform a parametric analysis (n=2,237), Box-Cox correction was applied in the study (Box & Cox, 1964). The findings of Box-Cox correction suggest the best power is the same as the logarithmic transformations (Moran & Solomon, 2012). A t-test was applied to examine the relationship of LOS and two age groups (over and under 65 years old). Analysis of Variance (ANOVA) was applied to examine the relationships between LOS and race, treatments, type of insurance and a Tukey test was also applied (Box & Cox, 1964; Kutner, 2005; Moran & Solomon, 2012).

61 44 Research Question Three: What nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes are frequently used by nurses on each of the four oncology units and overall? What is the relationship between groups of patients defined by the length of stay (<1 day, 1 day LOS < 3 days, LOS 3 days) and nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes overall and at each of the four oncology units? To answer Research Question Three, most frequently used NANDA-I diagnoses, NIC intervention, and NOC outcomes in oncology units were analyzed by descriptive analyses using frequencies and percentages. Chi-square tests were used to examine whether the most frequently used NANDA-I diagnoses, NIC intervention, and NOC outcomes differ by unit or by the three groups of LOS. Research Question Four: What are the most frequently used linkages of NANDA-I, NIC, and NOC in oncology? To answer Research Question Four, most frequently used linkages of NANDA- I, NIC, and NOC in oncology were analyzed by descriptive analyses using frequencies and percentages. Any pattern for nursing diagnoses, nursing interventions, or outcomes selected from the top 15 commonly used list from each were explored by a Chi-square tests with Fisher s exact tests (Der & Everitt, 2006), and a multiple comparisons, Bonferroni s method (Bailar & Hoaglin, 2012), was applied for adjusted p value. Research Question Five:

62 45 What patient characteristics are associated with positive, no change, or negative nursing-sensitive patient outcome change scores at discharge associated with patient s most common nursing diagnoses? To answer Research Question Five, a descriptive analysis was applied to address the distribution of outcome change scores for the most common selected NOC outcomes. Pain Level and Infection Severity were selected and analyzed for all units and by unit. These two NOC outcomes were selected because Pain Level was the most common used NOC outcomes in Unit G, Unit M and Unit H, and Infection Severity was the top NOC outcome in Unit A. For NOC outcome, Pain Level, the top NOC outcome for all units, its relationship with gender, age group, race, treatments, and type of insurance are also reported in a descriptive table. Human Subjects The proposed involvement of human subjects consists of participants discharged from four oncology units in the study hospital from June 1 to December 31, The data were retrieved from a computerized system and stored in a security data base. Patient identification information was protected and all data retrieved from the hospital records were stored in secure location by using a password protected in online drive at University of Iowa. None of data was stored in a hard copy. Identities were removed and replaced with an automated number. The approval from the University of Iowa Institutional Review Board (IRB) was granted for the study in March, 2011 (Appendix E).

63 46 Summary This was a descriptive retrospective study. The descriptive design is useful for documenting various characteristics in an oncology population to nursing to gather baseline data on these populations (Brink & Wood, 1998). The data were analyzed with Enterprise Guide 4.3 with SAS 9.3 (Davis, 2007). Data analysis was performed according to each research question. Characteristics of patient (Question One) were addressed by (a) frequencies and percentages for categorical variables, and (b) means, standard deviation, and ranges for continuing variables, each unit and for the overall sample in the oncology population. Box-Cox correction was performed for transforming value of LOS which is not normal distributed. ANOVA was performed and Tukey post hoc test was conducted to examine LOS by unit (Question Two) (Box & Cox, 1964; Kutner, 2005; Moran & Solomon, 2012). Commonly used nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes each unit and for the overall sample (Question Three), were analyzed by using frequencies of percentage. Differences of nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes each unit and for the overall sample, were analyzed using Chi-square tests. Descriptive statistics were described using (a) frequencies and percentages for each NANDA-I, NOC, and NIC each unit and for the overall sample in the oncology population. Frequencies of nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes were reported in percentages for all of the patients as well as by the individual units. The commonly used linkages of NNN (Question Four) were also analyzed by using frequencies or percentages. Chi-square tests with Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons were applied for examining pair patterns in each two NANDA-I

64 47 labels, two NOC labels, and two NIC labels. NOC outcome change scores for the most common NOC outcomes (Question Five) were analyzed using descriptive statistics. Chapter IV provides the results of the research.

65 48 CHAPTER IV DATA ANALYSIS AND RESULTS The results of the data analysis are presented in this chapter. Descriptive statistical analysis was used to report patient demographics for Question One. Three methods were considered when LOS as dependent variable was not normal distributed. Box-Cox correction was finally selected to answer whether length of stay (LOS) differs among units to address Question Two. To answer Question Three, descriptive statistical analysis was performed to determine the frequently used nursing diagnoses (NANDA-I), nursing interventions (NIC), nursing-sensitive patient outcomes, (NOC). A Chi-square method was applied to examine any difference of these frequently used SNTs among units and three groups with different LOS. Descriptive statistics and a Chi-square method were also applied to answer Question Four. The descriptive analysis was used to address the most frequent NNN linkages (NANDA-I NOC NIC) and a Chi-square method was used to examine a pattern among two of top 15 nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes. Descriptive analysis was applied to address outcome change scores by different groups: unit, age, treatment, and type of insurance. The study consisted of three informational resources: medical records, tumor registry, and nursing care plan summaries from a 762-bed Midwestern tertiary hospital. Planned nursing care (nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes) were manually retrieved from nursing care plan summaries in the nursing information system. The other two resources were directly retrieved from the EHRs (Electronic Medical Records and local Tumor Registry data). All data were stored

66 49 in Microsoft Office 2010 Excel spreadsheets and were analyzed by SAS, Enterprise Guide 4.3 with SAS version 9.3. Sample A total of 3,335 hospitalizations provided 2,671 unique patients since some of the 3, 335 patients having multiple admissions over the study period (June1 st, 2010 through December 31 st, 2010). Patients in the seven-month study period had an average of 1.2 admissions (SD=0.7, range= 1 11). Only first admissions for patients with a care plan were entered into the study for analysis. A total of 2,237 individual patients with a care plan, discharged from four specialty units with a diagnosis of cancer or who were under investigation for a possible cancer diagnosis were included in the data analysis. Analysis of the Research Questions Research Question One: What are the characteristics of the patients in the four oncology units of a large hospital? There are four categories of patient characteristics: Demographics (age, gender, race, marital status, years of education, and employment status), Disease information (site of tumor, duration of cancer since first diagnosed, stage of cancer, severity of illness, and risk of mortality), Hospitalization information (site of initial admission, oncology unit, length of stay, and discharge status), and a Cost-related variable (type of insurance).

67 50 To answer Question One, a descriptive analysis approach was used. Continuous variables, such as age, length of stay (LOS), and length of cancer diagnosis, were reported using means, standard deviation, and range. Categorical variables, such as race, oncology unit, etc., were reported by frequencies and percentage. Table 1 describes the demographics, disease information, hospitalization information, and cost-related variables for all oncology patients. Two variables collected for the study, site of initial admission and Medicare Severity-Diagnosis Related Groups (MS-DRGs) that were identified in the patient record and were not described in the study due to difficulties to identify them into meaningful categories for analysis. Demographics of Patients The average age of the patients in the sample was 55 years (SD=17, range from 18 to 99). The sample consisted of 1,408 females (63%) and 829 males (37%). The patients were primarily Caucasian (89%), female (63%), married (53%), with 12 to 16 years of education (61%), and retired (26%). Disease Information There were 1,300 (58%) patients with a documented cancer diagnosis (ICD-9 CM) in their medical records and 937 (42%) of patients were under investigation for a possible cancer diagnosis. Data from the Tumor Registry, such as length of time since first diagnosis, type of treatment(s) related to cancer, and stage of cancer were limited to malignant tumors and first records for patients documented in the Tumor Registry (n=541). Most patients had only one diagnosis of cancer (97%). There were 541 patients with a newly diagnosed malignant cancer within three months of diagnosis (M=3.3 months, SD=14.1 months, range = this admission to 18 years since cancer was first

68 51 diagnosed). Among these 541 patients, each patient received an average of 2.7 cancer treatments (SD=0.8, range=1 6). Only six categories of treatments related to cancer were collected. They were chemotherapy, radiotherapy, surgery, hormone therapy, immunotherapy, and/or other treatments. In the data analysis, hormone therapy, immunotherapy, and other therapies were grouping in one category due to fewer patients underwent these treatments. Only one fourth of the sample had documentation of the site of the tumors because the data were collected from the Tumor Registry and not all patients discharge from oncology units meet the criteria to be included in the Tumor Registry. For example, the Tumor Registry only collects information from patient with malignant cancer. No one cancer site dominated the data, The most common primary site of the cancer for the patients in this study was the head and neck (n=137, 6%) but this only accounted for 6 % of the patients. Other cancer sites included the digestive system (n=121, 5%), genitourinary sites (n=121, 5%), lymphoma (n=103, 5%), Leukemia/Other (n=76, 3%), Neuroendocrine(n=31, 1%), other site (n=5, <1%), benign(n=164, 7%), carcinoma in Situ (n=16, <1%), metastasis (n=8, <1%), unknown primary (n=8, <1%), with cancer history (n=81, 4%), and under investigation (n=937, 42%). Only a total of 548 records from the Tumor Registry with malignant cancer were documented the stage of cancer. Among these patients, the frequencies for patients stage of the tumor were recurrent cancer or non-specified type (n=141, 26%), stage I (n=130, 24%), stage III (n=89, 16%), not applicable (n=83, 15%), stage IV (n=44, 10%), stage II (n=42, 8%) and stage 0 (n=8, 1%). The rest of patients discharged from the four oncology units were either under investigation for a cancer diagnosis, newly diagnosed or had benign results that were not

69 52 documented in the Tumor Registry. Based on severity of illness, 68% of patients were mildly to moderately ill; based on risk of mortality, 80% were located in the mild to moderate categories. Hospitalization Information Length of stay (LOS) in an oncology unit was used to answer Question Two and Question Five in this study. Length of stay (LOS) was defined as the duration of the hospitalization from the date admitted to an oncology unit until the date of discharge (SD= 3.8, range= days). The Gynecology, Oral Surgery, and Otolaryngology Unit (Unit G) had the highest volume of patients in this study (n=1022, 46%), followed by the Medical Surgical Oncology Unit (Unit M, n=707, 32%), the Hematology/Oncology and Palliative Care Unit (Unit M, n=463, 21%), and the Adult Leukemia and Bone Marrow Transplant Unit (Unit A, n=45, 2%). Four categories were used to describe the discharge status of patients: The majority of patients returned to home or self-care (82%, n=1840,); 4% (n=84) returned to home with health care support services; 10% (n=205) were referred to other facilities; and 4% (n=98) died during the hospitalization. Additionally, 29% patients were admitted for surgery and 59% received more than two treatments related to cancer. Type of Insurance The main types of insurance used by patients in this study was private insurance companies (n= 1190, 52%), Medicaid (n=785, 35%), followed by Medicare (n=241, 11%), and self-pay/uninsured (n=21, 1%). Medical Service - Diagnosis Related Group (MS-DRG), another cost-related variable collected, was not addressed to answer this research question due to the variety of categories assigned to these patients.

70 53 Research Question Two: What is the length of stay for patients in each of the four oncology units? Does length of stay differ by units compared to overall length of stay for the other three units? Are there any differences in length of stay between age groups (over and under 65 years); medical treatment groups, such as patients receiving surgery procedures (SP), radiotherapy (RT), chemotherapy (CT), other treatments such as hormone therapy, or a combination of treatments above; type of insurance (Medicare, Medicaid, self-pay/uninsured, or private insurance)? LOS for All Units and by Unit To answer Question Two, length of stay (LOS) in oncology unit was used as a dependent variable and a normality test was performed. LOS in this study appeared as a positive skewed distribution and a reverse J-shaped curve (See Figure 2). In order to deal with the violation of the assumption of a normal distribution, three methodologies were applied (Box Correction, negative binomial regression model, and non-parametric analysis). The sample size was sufficient to perform ANOVA with violation of assumption of normal distribution after Box correction. Box-Cox data transformation using SAS 9.3 was performed. The Box-Cox procedure results in best lambda (λ=-0.03), which suggested a logarithmic data transformation (λ=0 as defined). To prevent a negative value, a parametric constant was used (log (LOS+1)). Figure 3 shows the

71 54 distribution after data transformation (Box & Cox, 1964; Kutner, 2005; Moran & Solomon, 2012). Figure 2 shows distribution of length of stay (LOS) as positive skewed to the right (N=2,237, M=3.7, SD=4.6, Q1=1, Median=2, Q3=4, Mode=1, Skewness=4.4, Kurtosis=32.1, Kolmogorov-Smirnov <.01). It is common that LOS showed skewness to the right. Data transformations (e.g., logarithmic methods, square root) would be commonly applied when assumption of normality was violated (Box & Cox, 1964; Kutner, 2005; Moran & Solomon, 2012). Figure 3 shows the histograph for distribution of LOS after Box-Cox correction (Box & Cox, 1964; Kutner, 2005; Moran & Solomon, 2012). The Box-Cox procedure suggests the best power for data transformation as, which is the same as the logarithmic transformation. To simplify the procedure of data transformation and avoid a negative value, a constant value 1 was used. Logistic data transformation, LOG (LOS+1), was used. After data transformation, Sknewness and Kurtosis for the distribution of LOS were corrected into an inacceptable range (N=2,237, M=1.3, SD=0.6, Q1=0.7, Median=1.1, Q3=1.6, Mode=0.7, Skewness=0.9, Kurtosis=0.6, Smirnov <.01). However, the distribution of LOS is still asymmetric. Table 2 and Table 3 report descriptive statistics of LOS with different groups based on age, treatment, race, and type of insurance in all and within units. A t-test was applied to the two age groups (18 years < age < 65 years, and 65 years) and Analysis of Variance (ANOVA) was conducted on groups with different units, treatments, race, and type of insurance groups. Tukey post-tests were selected for pairwise comparisons. Type of unit (p<.0001), and treatment (p<.0001), age group (p=.01), and type of insurance

72 55 (p=.001) were found significant in the sample (Table 2 and Table 3). Unit A had significant longer LOSs than the other three units. Unit M had longer LOSs than Unit G. Patients admitted to oncology units and those whose age was over or equal to 65 had longer LOSs than younger adults. For LOS by the groups with different treatments, the patients received combined treatments with surgery related to cancer (SP), chemotherapy (CT), radiotherapy (RT), and had longer LOSs than those patients receiving other treatments. Patients on Medicaid had longer LOSs than those with other types of insurance. Figure 2 The Distribution of Length of Stay (LOS) Before Data Transformation

73 56 Figure 3 The Distribution of Length of Stay (LOS) After Data Transformation Research Question Three: What nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes are frequently used by nurses on each of the four oncology units and overall? What is the relationship between groups of patients defined by the length of stay ( 1 day, 1 day LOS < 3 days, LOS 3 days) and nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes overall and on each of the four oncology units? Modified Nursing Diagnoses, Interventions, and Outcomes in the EHRs Modified nursing diagnoses, interventions, and outcomes refer to SNTs used in the study hospital while are not labels in current version of NANTA-I, NIC, or NOC. The existing modified nursing diagnosis, interventions, and outcomes were resulted from two reasons: out-of-date application system for nursing documentation system, and the

74 57 specific need of these SNTs in the study hospital. The study hospital has a history of using NANDA-I diagnoses, NIC interventions and more recently, NOC outcomes. The versions in use for NNN in the information system of the study hospital were implemented and updated in 2009, and are based on the following versions: NANDA-I ( ), NIC (5th ed.), and NOC (4th ed.). However, the latest version of NANDA-I nursing diagnoses was released in the spring, 2012, and the hospital has not updated the system to incorporate the new NANDA-I diagnosis. The study hospital modified several labels for NANDA-I diagnoses, NIC interventions and NOC outcomes with additional diagnosis concepts and limited to what was essential for their practice sites to meet the needs. For example, the study hospital uses a NANDA-I nursing diagnosis as a global term, Deficient Knowledge, and delineated it into three more specific nursing diagnoses: Deficient Knowledge Pre/Post Procedure/Surgery, Deficient Knowledge, Disease Process, and Deficient Knowledge, Insulin Therapy. The first one is a NANDA-I nursing diagnosis and the other four specify the deficient knowledge related to the patient. Based on NANDA-I taxonomy, Self-Care Deficit is divided into four concepts: Bathing: Self- Care Deficit, Dressing: Self-Care Deficit, Feeding: Self-Care Deficit, and Toileting: Self- Care Deficit; while the study hospital included a global diagnosis: Self-Care Deficit, which was not approved as a NANDA-I nursing diagnosis. Several nursing diagnoses were retired from the current edition of NANDA-I classification (Herdman, 2012) but were since being used in the in the hospital software application. These nursing diagnoses were Disturbed Thought Processes, and Disturbed Sensory Perception and the more specific ones of: Disturbed Sensory Perception, Visual,

75 58 Disturbed Sensory Perception, Kinesthetic, and Disturbed Sensory Perception, Auditory. Ineffective Tissue Perfusion is a diagnosis which specifies an area of focus such as Cerebral, Cardiac, Pulmonary, Renal or Peripheral. In the current edition only Ineffective Tissue Perfusion Peripheral is an accepted NANDA-I nursing diagnosis. Four nursing diagnoses used in the study hospital, Mood Alteration: Depression, Mood Alteration Mania, Inadequate Oral Food Beverage Intake, and Risk for Withdrawal: Alcohol/Drugs, have never been approved NANDA-I nursing diagnosis. The modifications for nursing interventions or nursing-sensitive patient outcomes are also described when reporting their frequencies. Variations in the use of terms were expected in this study since the data were from In many cases it took considerable time for the vendor system to update the content as new editions of the classifications were published. A descriptive analysis was used to answer Question Three: the most frequently used nursing diagnoses, interventions, and outcomes for all units, by unit, and by three groups with different LOSs. First, the average number of nursing diagnoses (NANDA-I), nursing interventions (NIC), nursing-sensitive patient outcome (NOC), and frequencies of NOC outcome ratings for a patient are reported in Table 3. Several tables for nursing diagnoses (NANDA-I), nursing interventions (NIC), and nursing-sensitive patient outcome (NOC) are reported in order: domain and class (Table 4, Table 6, and Table 8), ranking for all units (Table 5, Table 7, and Table 9), ranking by unit (Table 10, Table 12, and Table 14), and ranking found significantly different by unit (Table 11, Table 13, and Table 15). Three tables (Table 16, Table 18, and Table 20) report frequencies and percentage of nursing diagnoses (NANDA-I), nursing interventions (NIC), and nursing-

76 59 sensitive patient outcome (NOC) by three groups based on LOS and another three tables (Table 17, Table 19, and Table 21) report significantly found different by LOS group from the earlier three tables. Table 22 reports the top ten NNN linkages. Three appendixes report the links between NANDA-I and NIC, the links between NANDA-I and NOC, and the links between NOC and NIC. Table 23, Table 24, and Table 25 report pattern between two nursing diagnoses, two nursing interventions, or two outcomes, respectively. Table 26 and Table 27 report outcome change scores for Pain Level and Infection Severity. In these four oncology specialty units, only 84% of patients (n=3,335) had a plan of care documented in the care plan summaries retrieved from the EHRs. In Table 3, nurses on average identified 3.1 nursing diagnoses (SD=2.5, range=1 28), 6.3 nursing interventions (SD=5.1, Range=2 56), and 3.7 nursing-sensitive patient outcomes (SD=2.9, range=1 31) for a patient. Nurses also rated the outcomes of their patients 9.3 times on average (SD=13.4, range=1 197) but this varied greatly across different outcomes. Table 4 reflects all nursing diagnoses, interventions, and outcomes. Even though modified SNTs are not labels in current version of NAN-I, NIC, or NOC, they were used in these four oncology units as SNTs. These modified nursing diagnoses, interventions, and outcomes will be noted by superscript letter a or b in relevant tables and are also described in the notes after the tables. NANDA-I Nursing Diagnoses: All Units Table 4 describes ranking of frequently used nursing diagnoses by domains and classes; while Table 5 describes the ranking of nursing diagnoses for all units. Two type of percentages were both used. The rankings of both tables are all decided by the amount

77 60 of nursing diagnoses for all units for the seven-month study period. A total of 2,237 patients had 7,002 nursing diagnoses over the 7-month study period. A total of 88 different NANDA-I nursing diagnoses were found for these four units. Table 5 describes ranking by frequencies of NANDA-I nursing diagnoses for the four units. Acute Pain was the top ranked NANDA-I nursing diagnosis. In Table 5, it accounted for 25% of all nursing diagnoses and 78% of the patients in the study were diagnosed with Acute Pain. Risk for Infection (n=710) accounted for 10% of all nursing diagnoses, and 32% of the patients and ranked second. Other nursing diagnoses in the top 10 frequency list were Nausea, Impaired Skin Integrity, Risk for Falls, Deficient Knowledge: Pre/Post Procedure/Surgery, Activity Intolerance, Deficient Knowledge: Disease Process, Anxiety, and Imbalanced Nutrition: Less than Body Requirements, based on overall percentages of the total list of nursing diagnoses across all units. The two modified nursing diagnoses, Deficient Knowledge: Pre/Post Procedure/Surgery (rank: 6), and Deficient Knowledge: Disease Process (rank 8), were more frequently reported than its global term of Deficient Knowledge (rank 26) in this study sample. The rest of the modified nursing diagnoses appeared to be 1% or less of the total nursing diagnoses selected. Nurses had used Ineffective Tissue Perfusion: Pulmonary on 97 patients (1% of all nursing diagnoses, 4% of all patients, rank: 17) over the seven-month study period. There is no current NANDA-I relevant to Ineffective Tissue Perfusion: Pulmonary. Another retired group of diagnoses was selected in the study was Disturbed Sensory Perception specified as Visual, Kinesthetic, and Auditory. A total of seven patients had been diagnosed with these diagnoses. Disturbed Thought

78 61 Process is another retired NANDA-I nursing diagnosis and was used on 15 patients in the study. In addition, nurses used nearly 1% of a global descriptive term of Deficit Self- Care, rather than approved and specific NANDA-I nursing diagnoses, such as Bathing: Self-Care Deficit, Dressing: Self-Care Deficit, Feeding: Self-Care Deficit, and Toileting: Self-Care Deficit. The four nursing diagnoses, Risk for Withdrawal: Alcohol/Drugs (n=10), Mood Alteration: Depression (n=2), Mood Alteration: Mania (n=1), Inadequate Oral Food Beverage Intake (n=1), are non-nanda-i nursing diagnoses. NIC Interventions: Domains and Classes Table 6 reports domains and classes of the taxonomy of NIC. Two types of percentages were used and their calculated were the same as the nursing diagnoses. The first percentage (% 1, a sum of nursing interventions on the basis of all units or cumulative frequencies of any time of nursing interventions have been documented even though it can be on the same patients) were calculated twice, while the second percentage (% 2, a nursing intervention that individual had received, and any documented duplicated nursing interventions would be counted as one) was calculated once. Therefore, a discrepancy may occur in % 1 and % 2. Moreover, in Table 4, only one column report as the number of diagnosis or the number of patient had that specific nursing diagnosis; while there were two columns of numbers are reported in Table 6. The discrepancy of numbers was due to duplicate interventions documented under different diagnoses in the same individual. In a word, some patients may have the same interventions linked with different nursing diagnoses, for example, Pain Management for both Acute Pain and Chronic Pain. The first number (n 1 ), which refers to the count all interventions applied to each diagnoses in

79 62 independent patient, including duplicate interventions linked to different diagnoses; and the second number (n 2 ), which means the count of any of 105 interventions presented in individual patient, excluding duplicate interventions. For example, a patient had the nursing diagnoses of Acute Pain and Chronic Pain. Pain Management were both documented and linked to either Acute Pain or Chronic Pain. Additionally, Health Education, was found the only modified nursing intervention and was marked with superscripted letter a in the Table 6. The taxonomy of NIC has seven domains: Physiological: Basic, Physiological: Complex, Behavioral, Safety, Family, Health System, and Community. In the study, none of the 100 unique NIC interventions were from the Health System or Community domains. Table 6 reports the distribution of the NIC interventions in their domains (n=5) and classes (n=22). Ranking of domains for NIC intervention were Behavioral, Physiological: Complex, Physiological: Basic, Safety, and then Family by using the frequencies of NIC labels in domains. However, if individual NIC label is concerned, the most frequently performed NIC interventions was Fall Prevention (n=742), which is relevant to patient safety. Although domain of Safety had fewer numbers of NIC intervention labels in domains than that in the domain of Behavioral, Physiological: Complex, and Physiological. It is obvious that Fall Prevention, Infection Protection, Infection Control, and Pressure Management in the class of Risk Management under the domain of Safety showed higher frequencies, which implies that safety has been emphasized in the four oncology units.

80 63 NIC Interventions: All Units The top nursing interventions identified across four oncology units are described in Table 7. These nursing interventions were selected and based on the nursing diagnoses identified for the patient and the outcomes that were chosen. Two types of percentage (% 1 and % 2 ) were used to describe frequencies of NIC interventions: the first percentage (% 1 ) was calculated from a frequency of the intervention over a total of frequencies of all interventions for all units (N=11,804), and the second percentage (% 2 ) was calculated from a person with an intervention over total patients (N=2,237). The top ten nursing interventions are: Pain Management, Fall Prevention, Infection Protection, Infection Control, Nausea Management, Nausea Management, Teaching: Procedure/Treatment, Analgesic Administration, Skin Surveillance, Wound Care, and Pressure Management. There are six NIC interventions relevant to patient safety or risk management in the top ten. They are Fall Prevention, Infection Protection, Infection Control, Skin Surveillance, Wound Care, and Pressure Management. There only 2% of patients received nursing interventions of Oral Health Restoration. There are 18% of patients who received nursing interventions relevant to nutrition: Nutrition Management (8%), Nutrition Therapy (8%) and Nutritional Monitoring (2%). NOC Outcomes: Domains and Classes The NOC taxonomy has seven domains at the highest level of abstraction. They are Functional Health, Physiologic Health, Psychosocial Health, Health Knowledge & Behavior, Perceived Health, Family Health, and Community Health. Table 8 presents the NOC outcomes selected for patients in this study in their Domain and Class structure. Two types of percentages are also reported and their calculations are similar with nursing

81 64 diagnoses and outcomes. Two types of percentage (% 1 and % 2 ) were used to describe frequencies of NOC outcomes: the first percentage (% 1 ) was calculated from a frequency of the outcome over a total of frequencies of all outcomes for all units (N=8,197), and the second percentage (% 2 ) was calculated from a person with an outcome over total patients (N=2,237). Some patients may have same outcomes linked with different nursing diagnoses, for example, Pain Level for Acute Pain and Chronic Pain. Therefore, the column of number (n 1) refers to the count all outcomes applied to each o in independent patients (N=2,337) including duplicate outcomes linked to different diagnoses; the column of number (n 2) refers to the count of any of 81 outcomes presented individual patient, excluding duplicate outcomes. Therefore, a discrepancy may occur in n 1 and n 2. In addition, Oral Intake was the only modified outcomes in the study. The study identified 81 individual NOC outcomes and a total of 8,197 NOC among 2,237 patients over the seven-month study period. NOC outcomes selected were distributed over six domains of NOC. No outcomes from the Community Health domain were selected by nurses. The 8,197 outcomes were distributed in the six domains of Physiologic Health (n=2613, 32%), Perceived Health (n=2472, 30%), Health Knowledge & Behavior (n=1793, 22%), Functional Health (n=947, 12%), Psychosocial Health (n=337, 4%), and Family Health (n=35, <1%). The top domains that contained the most outcomes selected in this study were: Physiologic Health (n=37), Health Knowledge & Behavior (n=14), Functional Health, (n=12), Psychosocial Health (n=12), and Perceived Health (n=5), and Family Health (n=2). Symptom Status, one of the classes under the domain of Perceived Health had 30% of all NOCs (such as Pain Level) selected for oncology patients in the study. Eighty-one of the outcomes were distributed across 24 of

82 65 the 31 NOC classes. The class of Cardiopulmonary had the most individual outcomes (n=9, Table 8), but the frequencies (6%) were less than the class of Symptom Status (7%) which had four individual outcomes. NOC Outcomes: All Units The ranking of the 81 NOCs identified in the study can be found in Table 10. The top three NOC outcomes were Pain Level, Infection Severity, and Nausea and Vomiting Severity, corresponding to the top NANDA-I diagnoses, which were Acute Pain, Risk for Infection, and Nausea. Instead of Tissue Integrity: Skin and Mucous Membranes corresponding to the 4 th rank diagnosis in NANDA-I, Impaired Skin Integrity, the 4 th NOC was Knowledge: Treatment Procedure. The difference was slightly over 3% evaluated by the nursing-sensitive outcomes of Knowledge: Treatment Procedure then Tissue Integrity: Skin and Mucous Membranes. In general, NIC and NOC would have been corresponding to their relevant NANDA-I if nurses had made correct decision in planning care for patients. For Example, the nursing intervention, Pain Management and outcome, Pain Level are associated with the nursing diagnoses, Acute Pain, but Risk for Infection. There might be one or two NOC outcomes concurrently linked to the same NANDA-I for a person and also several NOC outcomes linked to a NIC intervention (range=1 4). Therefore, the ranking may be slightly altered due to the second NOC, especially for those linked to the most frequently identified NANDA-I diagnoses, such as Acute Pain. However, if the second NOC was not always frequently followed by the first NOC, or not always paired with the first NOC, the ranking between the first and second NOC could be far away from each other in the ranking list, or vice versa. For example,

83 66 Pain Level and Pain Control were common NOC outcomes used for Acute Pain, the top NANDA-I diagnosis. Pain Level ranked first and Pain Control ranked 6 th. More patients (57%) had the NOC, Pain Level, without Pain Control. If two NOC outcomes for the same NANDA-I diagnosis were almost always selected together, the rank may be closer or even next to each other in the ranking list, such as the outcomes, Knowledge: Fall Prevention and Fall Prevention: Behavior (ranked 7 th and 8 th ) and their relevant NANDA-I diagnosis, Risk for Falls (ranked 5 th in NANDA-I). In the study, patients with the same nursing diagnoses may have different combinations of nursing interventions and outcomes, and these combinations were easily recognized and represented by the format of SNTs. Another potential factor for the change of the top NIC interventions or NOC outcomes rankings corresponding to their NANDA-I diagnosis was different NOC outcomes were selected for frequent NANDA-I diagnoses. For patients who experienced Acute Pain or Chronic Pain, their common NOC outcome was Pain Level. Pain Control was the second common NOC outcome selected, corresponding to the diagnosis Acute Pain, and Pain: Disruptive Effects was second common NOC outcome, corresponding its NANDA-I diagnosis of Chronic Pain. The outcome Pain: Disruptive Effects ranked 33th and its corresponding NANDA-I diagnosis, Chronic Pain, ranked 18 th. Other well-known problems that patients with cancer usually experience had the following rankings: Nutritional Status (8%, rank: 11), Sleep (<1%, rank: 36), Spiritual Health (<1%, rank: 38), and Depression Level (<1%, rank: 71). Anxiety Level was the only top ten NOC outcome relevant to the patient s psychosocial dimension that oncology nurses selected for their patients, which account for only 9 % for the overall sample. Malnutrition can be

84 67 an issue for hospitalized oncology patients (Gupta et al., 2011, Laky et al, 2010, Lim et al., 2011). In the study, only 9% of patients Nutritional Status, as a nursing-sensitive patient outcome, had been evaluated by nurses. In addition, only 2% of oncology patients in the four units had been evaluated with their Oral Hygiene. In the acute setting with LOS equal to 3.7 days on the average in the study sample, it was shorter than the LOS reported in similar oncology populations. Compared to LOS (Range =2-20) in the study of Mansour et al (2011), LOS in the study is slightly shorter. NANDA-I, NIC, and NOC by Unit NANDA-I Nursing Diagnoses by Unit In Table 10, Chi-square tests with Fisher s exact tests (Der & Everitt, 2006) were performed to answer question Three: Do frequencies of NANDA-I diagnoses differ by unit? A post-test Bonferroni s method (Bailar & Hoaglin, 2012) for multiple comparisons (n=105) was applied to adjust the p value at.05. Table 10 shows frequencies and percentage of nursing diagnoses for patients in the four units that were statistically significant. For example, 92% of the patients admitted to the Gynecology, Oral Surgery and Otolaryngology Unit (Unit G) had significantly more patients with Acute Pain than the other units. Acute Pain was the top nursing diagnosis in Gynecology, Oral Surgery, and Otolaryngology Unit, Hematology/Oncology and Palliative Care Unit, and Medical Surgical Oncology Unit. However, Risk for Infection was the most commonly used nursing diagnosis in the Adult Leukemia and Bone Marrow Transplant with Acute Pain ranked fourth in this unit. Acute Pain, Risk for Infection, and Nausea were all in the top five list of nursing diagnoses in the four units. In general, the top ten lists of nursing diagnoses in the four units were similar to each other.

85 68 Table 11 provides the ranking of the 87 nursing diagnoses in each unit. There were 32 nursing diagnoses found significant by unit in Table 10. Compared to the ranking of percentages for patients with nursing diagnoses in the other three units in Table 10, in Unit A, the NANDA-I diagnosis, Impaired Skin Integrity, is less frequently used, while Fatigue, and Impaired Oral Mucous were more often in the plan of care. Among the NANDA-I diagnoses that were significantly different by unit, Acute Pain was the top NANDA-I diagnosis in Unit G, Unit H and Unit M and had a higher rank than Unit A (rank 4, Table 11). The nursing diagnosis, Risk for Falls in Unit H rank ranked third but was ranked higher in Unit M, Unit A and Unit G. NIC Interventions by Units In Table 12, Chi-square tests with Fisher s exact tests (Der & Everitt, 2006) were performed to answer question Three: Do NIC interventions differ by unit. A post-test Bonferroni s method (Bailar & Hoaglin, 2012) for multiple comparisons (n=105) was applied to adjust the p value at.05. The frequencies and percentage of overall nursing interventions on each unit are reported in Table 12. There were 43 nursing interventions found significantly different by unit. Table 13 reports the ranking of the 43 nursing interventions. Table 13 shows the ranking of the 43 NIC interventions on each unit. Pain Management was the top NIC intervention in Unit G, Unit H, and Unit M. Infection Protection and Infection Control were the top one and two interventions selected in Unit A. The findings corresponded to the NANDA-I diagnoses Pain Management, Infection Protection, and Infection Control were the most frequently chosen interventions for the plan of care on these units. Nausea Management ranks second in Unit G and third in Unit

86 69 A, seventh in Unit H, and tenth in Unit M. Nausea Management was more frequently used in the plans of care on Unit G and Unit A. Teaching Procedure/Treatment was the top interventions in Unit A, with a higher ranking than Pain Management, which means this Deficient Knowledge may be emphasized in this specialty unit (Adult Leukemia and Bone Marrow Transplant).. It also is very common in the other three (Unit G: rank 5, Unit H: rank 4, Unit M, rank 6). Skin Surveillance and Wound Care have higher ranks (between rank 5 to rank 7) in Unit G and Unit M. It is surprising that Skin Surveillance (rank 23) and Wound Care (rank 29) was in a lower rank for patients that had a longer LOS in Unit A. This may be because there were only two patients in Unit A that had the NANDA-I diagnoses, Integrity Skin Integrity and none of patients in this Unit had a problem with Physical Mobility Intolerance. In Unit A, the interventions Energy Management, and Activity Therapy were ranked higher than in other three units. Oral Health Restoration is another intervention provided by nurses in Unit A that has a higher rank than the other units. Fall Prevention was more frequently planned in Unit H and Unit A than Unit M and Unit G. One common feature for the four units is that Nutrition Management is not in the top ten ranking of NIC interventions found significantly different by unit. NOC Outcomes by Unit A Chi-square test with Fisher s exact test (Der & Everitt, 2006) was performed to answer Question Three: Do frequencies of NOC outcomes differ by unit? Table 14 reports the frequencies and percentages for NOC outcomes by unit. The order of NOC outcomes in Table 15 was the same ranking as Table 14. There were 46 NOC outcomes reported differently by unit. In Gynecology, Oral Surgery, and Otolaryngology Unit,

87 70 compared to overall ranking, there were seldom changes. In Hematology/Oncology and Palliative Care Unit, compared to the other three units there was a slight decline in the percentage of use of Tissue Integrity: Skin and Mucous Membranes. In this unit the percentage of use increased with Pain Control, Knowledge: Fall Prevention and Fall Prevention Behavior and declined again on the outcome of Activity Tolerance. In the Medical Surgical Oncology Unit, the percentage of use compared to the other three units but use declined on Nausea and Vomiting Severity and use increased on Knowledge: Treatment Procedure. It revealed that this unit had a greater priority or more frequent outcomes on Knowledge: Treatment Procedure than Nausea and Vomiting Severity. Table 15 reports the ranking of NOC outcomes found significantly different by unit. Pain Level was the top NOC outcome on three units but on the Unit A, Pain Level ranked third. Infection Severity ranked first on Unit A and Knowledge: Treatment Procedure was the second ranked NOC outcome due to those patients admitted for procedures on this specialty unit. In Unit G and Unit A, Nausea and Vomiting Severity was in the second and third rank, however, in the other two units, Unit H and Unit M, it ranked in seventh and eighth place. NANDA-I, NIC, and NOC by LOS To answer Question Three: Do frequencies of nursing diagnoses, interventions, and outcomes differ by LOS groups, Chi-square tests with Fisher s exact tests (Der & Everitt, 2006) were performed. A post-test Bonferroni s method (Bailar & Hoaglin, 2012) was applied to adjust the p value at.05. Chi-square tests for those observations less than 20 were not applied and p value were not calculated. Therefore, only frequencies and percentages were presented. Since the distribution of LOS was skewed to the right, instead of using the mean of LOS (M=3.7) as a cutoff time, two cutoff points for LOS (24

88 71 hours and 3 days), were selected. The first cutoff point was chosen because of study interests and the second cutoff was because it was close to the mean and equalizes the number between patients with less and more than three-day LOSs. Patients were categorized in three groups. 1) Group One were patients admitted less than 24 hour, 2) Group Two were patients hospitalized from one to three days, and 3) Group Three were patients that stayed more than three days. Table 16, Table 17, and Table 18 describe the frequencies of nursing diagnoses (NANDA-I), interventions (NIC), outcomes (NOC), and their relationship with LOS. NANDA-I Nursing Diagnoses by LOS Table 16 shows that nurses had planned care for 61 patients (3%) who were admitted for less than 24 hours (Group One). Specifically, 40 (66%) of patients in this group had a nursing diagnosis of Acute Pain. Nearly 80% of patients had Acute Pain in Group Two, and 76% in Group Three. Acute Pain did not show any different percentage across the three LOS groups, which means human response to Acute Pain for cancer patients was consistent across hospitalizations regardless of length of stay. There were 15 out of the 88 NANDA-I diagnoses that had a significant relationship related to LOS in the three categories. Among the 15 diagnoses, they all appeared in the same pattern. The patients with longer LOSs had higher frequencies of those specific nursing diagnoses (Group Three > Group Two> Group One) except for the diagnosis of Diarrhea which was documented in the same number in the Group One and Group Two (n=3). For these 15 nursing diagnoses, there was a significant difference in three LOS groups: three related to the respiratory system (Ineffective Airway Clearance, Impaired Gas Exchange, and Ineffective Tissue Perfusion: Pulmonary), three related to the gastrointestinal system

89 72 (Nausea, Risk for Constipation, and Diarrhea), two related to physical activities (Impaired Physical Mobility and Activity Intolerance), and others(deficient Knowledge, Disease Process, Anxiety, Imbalanced Nutrition: Less than Body Requirements, Risk for Impaired Skin Integrity, Chronic Pain, Risk for Imbalanced Fluid Volume, and Diarrhea) However, 12 of them showed percentages (column%) in Group One that were greater than Group Two. They were Activity Intolerance, Deficient Knowledge, Disease Process, Anxiety, Imbalanced Nutrition: Less than Body Requirements, Ineffective Airway Clearance, Impaired Gas Exchange, Risk for Impaired Skin Integrity, Impaired Physical Mobility, Ineffective Tissue Perfusion: Pulmonary, Chronic Pain, Risk for Imbalanced Fluid Volume, and Diarrhea. Three nursing diagnoses related to the respiratory system were found significantly related to LOS and were all in this category. NIC Interventions by LOS In Table 18, 20 out of the 100 NIC interventions demonstrated significant relationships between the three LOS groups. These nursing interventions were aligned with nursing diagnoses also found to be related to LOS. They were Fall Prevention, Infection Protection, Infection Control, Nausea Management, Energy Management, Activity Therapy, Exercise Promotion: Strength Training, Anxiety Reduction, Nutrition Management, Nutrition Therapy, Ventilation Assistance, Teaching: Disease Process, Bowel Management, Diet Staging, Airway Management, Airway Suctioning, Acid-Base Management: Respiratory Acidosis, Acid-Base Management, Fluid Monitoring, and Circulatory Care: Venous Insufficiency. They also showed the same pattern with higher frequencies in longer LOSs. However, 14 out of 20 showed higher column percentage in Group One (less than 24 hour admission) than in Group Two (Stayed one to three days).

90 73 They were Fall Prevention, Energy Management, Activity Therapy, Exercise Promotion: Strength Training, Anxiety Reduction, Nutrition Management, Nutrition Therapy, Ventilation Assistance, Teaching: Disease Process, Airway Management, Airway Suctioning, Acid-Base Management: Respiratory Acidosis, Acid-Base Management, and Fluid Monitoring. LOS was not associated with patients who received the intervention Pain Management. NOC Outcomes by LOS In Table 20, 15 NOC outcomes showed significant differences in the three groups based on LOS. Since nursing interventions and nursing-sensitive patient outcomes aligned with nursing diagnoses, similar findings were expected. They were Infection Severity, Nausea and Vomiting Severity, Knowledge: Treatment Procedure, Knowledge: Fall Prevention, Fall Prevention: Behavior, Activity Tolerance, Anxiety Level, Nutritional Status, Knowledge: Illness Care, Gastrointestinal Function, Respiratory Status: Airway Patency, Respiratory Status: Gas Exchange, Tissue Perfusion: Pulmonary, Hydration, and Bowel Elimination. Frequencies of these NOC outcomes and their association with LOS also showed the same patterns: higher frequencies in longer LOSs. However, 12 NOC outcomes appeared to have higher percentage (column%) in Group One (less than 24 hour admission) than in Group Two (Stayed one to three days). While certain outcomes tended to be reported in patients with longer LOSs, such as Infection Severity, and Nausea and Vomiting Severity, Gastrointestinal Function. Some outcomes tended to be addressed in patients with either shorter LOS (< 24 hours) or longer LOS (>3 days). This can be described as the More-Less-Medium or the Less-More-Medium pattern. The first pattern

91 74 includes Knowledge: Fall Prevention, Fall Prevention: Behavior, Activity Tolerance, Anxiety Level, and Nutritional Status. The latter pattern includes Respiratory Status: Airway Patency and Respiratory Status: Gas Exchange, and Tissue Perfusion: Pulmonary and Knowledge: Illness Care, and Bowel Elimination. Patients evaluated with Pain Level had no significant difference based on LOS. Patients with longer LOSs tended to have Infection Severity as an outcome. Patients with LOSs of less than 24 hours tended to have outcomes related to the respiratory system, such as Respiratory Status: Airway Patency and Respiratory Status: Gas Exchange, and Tissue Perfusion: Pulmonary. Patients with longer hospitalizations tended to have outcomes related to the gastrointestinal system. Outcomes including Knowledge: Illness Care, Hydration, and Bowel Elimination were reported most frequently on patients admitted either less than 24 hours or longer than three days. Table 20 reports the top ranking of NOC outcomes found significant in the LOS groups. For patients with less than a 24 hour LOS, Knowledge: Treatment Procedure was the most commonly used NOC. Moreover, in the top five, three outcomes (Respiratory Status: Airway Patency, Respiratory Status: Gas Exchange, Tissue Perfusion: Pulmonary) were related to respiratory system. Infection Severity was the top NOC outcome when LOS was longer than one day. Nausea and Vomiting Severity was ranked as tenth when LOS was less than 24 hours; however, when admitted for more than one day, it moved up to the second place followed by Infection Severity. Anxiety Level was the only NOC outcome related to the psychosocial perspective. As LOS increased the ranking order of Anxiety Level moved from the 11 th, then 6 th and then to 8 th place. Knowledge: Fall Prevention and Fall Prevention: Behavior were ranked in the 6th and 7th places, and then moved up to the

92 75 3rd and the 4th, and then the 4th and 5th places as LOS increased. Nutritional Status was first ranked in 12th place, and then stayed at 7th if LOS was more than one day. Hydration stayed in the 13th or 14th placement. Knowledge: Illness Care ranged from 8th to 10th place in different groups of LOS. Bowel Elimination was stable in the 14th or 15th places in the different groups based on LOS. In general, the ranking of the NOC outcomes changed as LOS changed, however, the content of the top 15 NOC outcomes in the list were similar in the three LOS groups. Pattern of NNN Linkages Research Question Four: What are the most frequently used linkages of NANDA-I, NIC, and NOC in oncology? To answer Question Four, a descriptive analysis was used to report the top ten NNN linkages and Chi-square tests with Fisher s exact test (Der & Everitt, 2006) were performed to examine any two top 15 NANDA-I diagnoses, any two top 15 NIC interventions or any top 15 NOC outcomes that showed in the same patient in the study sample. The selected NANDA-I diagnoses, NIC interventions, and NOC outcomes were based on each of its own ranking over a total number of nursing diagnoses, nursing interventions, or nursing-sensitive patient outcomes for all units (Table 5, Table 7, and Table 9). If two of selected nursing diagnoses were either in a similar scope or both are frequently relevant to the same diagnoses, the second nursing diagnosis was excluded from the list. The same criterion was applied to selection of the top 15 NIC interventions, and the top NOC outcomes in the analysis. Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=105) were applied to adjusted p value at.05.

93 76 Table 21 reports the most frequently used linkages of NANDA-I, NIC, and NOC (n=1,735) by a descriptive analysis. Three tables from descriptive analyses report the links between nursing diagnoses, nursing intervention and nursing sensitive patient outcomes in detail are reported in Appendixes (APPENDIX E. LINKS OF NANDA-I AND NIC, APPENDIX F. LINKS OF NANDA-I AND NOC, and APPENDIX G. LINKS OF NOC AND NIC). The linkages of NANDA-I, NIC, and NOC were also reported from a descriptive analysis. The linkages of rank two and rank three are Risk for Infection and their two different NIC interventions (Infection Protection and Infection Control) and a NOC outcome (Infection Severity), whose frequencies are both less than half of the most frequent linkage combination. The linkage of Nausea (NANDA-I), Nausea Management (NIC) and Nausea and Vomiting Severity (NOC) rank fourth. The top five was another variation of Acute Pain, linking with the same NIC intervention (Pain Management) but a different NOC outcome (Pain Control). The top six and seven are the NANDA-I diagnosis of Impaired Skin Integrity, linking with two different NIC interventions (Skin Surveillance, and Would Care), and the same NOC outcome (Tissue Integrity: Skin and Mucous Membranes). The top eight and nine are the NANDA-I diagnosis of Risk for Falls, linking with the same NIC intervention (Fall Prevention), and two different NOC outcomes (Knowledge: Fall Prevention, and Fall Prevention: Behavior). The tenth rank is the NANDA-I diagnosis of Deficient Knowledge Pre/Post Procedure/Surgery, linking with a NIC intervention (Teaching: Procedure/Treatment), and a NOC outcomes (Knowledge: Treatment Procedure). In the top ten links, there are only six different nursing diagnoses (Acute Pain, Risk Infection, Impaired Skin Integrity, and Risk for Falls), and their links with either two different NIC interventions or two

94 77 different NOC outcomes. In the study, the NANDA-I diagnosis of Impaired Skin Integrity reports the most consistent links with four NIC interventions ( Wound care, Pressure Management, Skin Surveillance, and Pressure Ulcer Care). There were plenty of examples of two NOC outcomes linked to a NANDA-I diagnoses and will be detail in the study, for example, Pain Level and Pain Control. To examine any two combinations from the top 15 nursing diagnoses, interventions, and outcomes from Table 5, Table 7 and Table 9, Chi-square tests with Fisher s exact test (Der & Everitt, 2006) were applied and 105 comparisons were conducted with Bonferroni s method (Bailar & Hoaglin, 2012) for p value at.05. Frequencies, cell percentages, row percentages and column percentages of 105 comparisons between two nursing diagnoses, two nursing interventions and two outcomes are reported in Table 23, Table 24, and Table 25, respectively. Row percentages are the percentages for that value of the row variable corresponding to each of the column variable values. Colum percentages are the percentages for that value of the column variable corresponding to each of the row variable values. For example, in a cross-tabulation analysis (two-way table or 2 X 2 table) for two nursing diagnoses, Acute Pain (row variable) and Risk for Infection (column variable) in Table 23, there are 24% (cell percentage) of patients both with Acute Pain and Risk for Infections. For all patients with Acute Pain, there are 30% (row%) of them with Risk for Infection. For all patients with Risk for Infection, there are 74% (column%) of them with Acute Pain. In this study, two concurrent nursing diagnoses, or two concurrent nursing interventions, or two concurrent nursing-sensitive patient outcomes will be reported in the following sections. For example, there are 15 diagnoses selected (A to N) in the

95 78 analysis for concurrent pattern. The findings are addressed as one has nursing diagnosis A and then he or she may have the second one (B, or C, or D, or D, or N) if the findings are significant by Chi-square tests with Fisher s exact test. After a completion of diagnosis A, repeat the same procedure on diagnosis B and the second concurrent diagnosis with the Chi-square tests with Fisher s exact test from diagnosis B to diagnosis N. Patterns of Nursing Diagnoses (NANDA-I) Combinations In Table 23, in a total sample of 2,237 patients, 528 patients (24%) had the nursing diagnoses of Acute Pain and Risk for Infection. For patients having a nursing diagnosis of Acute Pain, 30% (row %) of them had a nursing diagnosis of Risk of Infection; and for patients having a diagnosis of Risk of Infection, 74% (col %) of them had a nursing diagnosis of Acute Pain. The finding is not significant after adjusted p at This means that patients having a nursing diagnosis of Acute Pain did not have a significant association with having the diagnosis of Risk for Infection. No association was found between patients with Acute Pain and other common nursing diagnoses, such as Risk for Falls, Activity Intolerance, Anxiety, Ineffective Airway Clearance, Risk for Imbalanced Fluid Volume, and Grieving). The findings of two concurrent nursing diagnoses on the same patients in the study may assist an initial interpretation of symptom cluster for this oncology population. Table 23 shows the patterns of nursing diagnoses. Patients with Acute Pain usually had additional diagnoses of Nausea, Impaired Skin Integrity, Deficient Knowledge Pre/post Procedure/Surgery, Imbalanced Nutrition: Less than Body Requirement, Fatigue, Risk for Constipation, or Urinary Retention. Patients with Risk for

96 79 Infection usually had Impaired Skin Integrity, Risk for Falls, Deficient Knowledge Pre/post Procedure/Surgery, Activity Intolerance, Anxiety, Imbalanced Nutrition: Less than Body Requirement, Fatigue, Risk for Constipation, and Risk for Imbalanced Fluid Volume. Patients with Nausea usually had Activity Intolerance, Anxiety, Imbalanced Nutrition: Less than Body Requirement, Fatigue, Risk for Constipation, or Urinary Retention. Patients with Impaired Skin Integrity usually had Risk for Falls, Deficient Knowledge Pre/post Procedure/Surgery, Activity Intolerance, Anxiety, Fatigue, Risk for Constipation, or Risk for Imbalanced Fluid Volume. Patients with Risk for Falls usually had Deficient Knowledge Pre/post Procedure/Surgery, Activity Intolerance, Anxiety, Imbalanced Nutrition: Less than Body Requirement, Risk for Constipation, Risk for Imbalanced Fluid Volume, or Grieving. Patients with Deficient Knowledge Pre/post Procedure/Surgery usually had Activity Intolerance, Anxiety, Risk for Constipation, Risk for Imbalanced Fluid Volume, or Urinary Retention. Patients with Activity Intolerance usually company with Anxiety, Imbalanced Nutrition: Less than Body Requirement, Fatigue, Risk for Constipation, Risk for Imbalanced Fluid Volume, Urinary Retention, or Grieving. Patients who experienced Anxiety also along with Imbalanced Nutrition: Less than Body Requirement, Fatigue, Ineffective Airway Clearance, Risk for Imbalanced Fluid Volume, or Urinary Retention. Patients who suffered from Imbalanced Nutrition: Less than Body Requirement also experienced Risk for Imbalanced Fluid Volume, or Urinary Retention. Patients who were diagnosed for Risk for Constipation also had problems with Risk for Imbalanced Fluid Volume, or Urinary Retention. Nursing diagnoses for Ineffective Airway Clearance or Grieving were often selected together. Risk

97 80 for Imbalanced Fluid Volume and Urinary Retention were also frequently diagnosed on the same patients. Pattern of Nursing-Sensitive Patient Outcomes (NOC) Combinations To answer Question Four: patterns of two nursing outcomes combinations, Chisquare tests with Fisher s exact tests (Der & Everitt, 2006) and Bonneroni s connection for a multiple comparison (Bailar & Hoaglin, 2012) were applied to explore two concurrent nursing-sensitive patient outcomes for the same patient. The 15 top NOC outcomes were selected after excluding the second outcomes that describe a similar scope or outcomes that are relevant to the same diagnosis. For example, Pain Control was excluded if Pain Level was selected. All the procedures were identical to previous methods for identifying patterns of NANDA-I diagnoses and patterns of NIC interventions. Table 26 shows the frequently paired nursing-sensitive patient outcomes (NOC). The findings are similar to the patterns of NIC interventions since they were based on related nursing diagnoses (NANDA-I). Patients with a NOC outcome of Pain Level had an additional outcome in the study sample. They are Nausea and Vomiting Severity, Tissue Integrity: Skin and Mucous Membranes, Nutritional Status, Urinary Elimination, or Coping. Patients with a NOC outcome of Infection Severity had an additional outcome, such as Knowledge: Treatment Procedure, Tissue Integrity: Skin and Mucous Membranes, Knowledge: Fall Prevention, Activity Tolerance, Anxiety Level, Nutritional Status, Hydration, and Oral Hygiene. Patients with a NOC outcome of Nausea and Vomiting Severity also had an additional NOC outcome, such as Activity Tolerance, Anxiety Level, Nutritional Status, Respiratory Status: Airway Patency, Urinary Elimination, or Oral Hygiene. Patients with a NOC

98 81 outcome of Knowledge: Treatment Procedure had an additional outcome, such as Tissue Integrity: Skin and Mucous Membranes, Knowledge: Fall Prevention, Activity Tolerance, Anxiety Level, Nutritional Status, Hydration, and Oral Hygiene. Patients with a NOC outcome of Tissue Integrity: Skin and Mucous had an additional outcome, such as Knowledge: Fall Prevention, Activity Tolerance, Anxiety Level, and Hydration. Patients with a NOC outcome of Knowledge: Fall Prevention had an additional outcome, such as Anxiety Level, Nutritional Status, Hydration, and Grief Resolution. Patients with a NOC outcome of Activity Tolerance had an additional outcome, such as Anxiety Level, Nutritional Status, Hydration, Urinary Elimination, Coping, or Oral Hygiene. Patients with a NOC outcome of Anxiety Level also had an additional NOC outcome, such as Activity Tolerance, Nutritional Status, Urinary Elimination, or Oral Hygiene. Patients with a NOC outcome of Nutritional Status had an additional outcome, such as, Hydration, Urinary Elimination, Coping, or Oral Hygiene. Patients with a NOC outcome of Respiratory Status: Airway Patency had an additional outcome, such as Grief Resolution. Hydration and Urinary Elimination are also chosen together in the patients of the study. Patterns of Nursing Interventions (NIC) Combinations To answer Question Four: any patterns of two NIC intervention combinations, Chi-square tests Fisher s exact tests (Der & Everitt, 2006) were applied or Bonferoni s correction (Bailar & Hoaglin, 2012) was used. All selection procedures or criteria were the same as those for the previous questions related to pattern of two nursing diagnoses combinations. Table 24 reports 105 multiple comparisons from top unique NIC interventions. NICs were selected from the top rankings; however, the researcher

99 82 excluded any second NIC with a similar concept. For example, Wound Care was excluded since Skin Surveillance was selected due to the study interest was between pairs with different concepts. Table 24 lists nursing interventions (NICs) frequently selected for the same patients. The findings were not different from what was found for nursing diagnoses due to nursing interventions were based on related nursing diagnoses (NORA- I). Table 24 describes the patterns of nursing interventions combinations. The patient who received the intervention Pain Managements also had Infection Protection, Nausea Management, Skin Surveillance, Energy Management, Nutrition Management, Bowel Management, Fluid Management, Urinary Retention Care. Patients received a nursing intervention as Fall Prevention, had also Infection Protection, Teaching: Procedure/Treatment, Skin Surveillance, Energy Management, Anxiety Reduction, Nutrition Management, Temperature Regulation, or Oral Health Restoration. Patients receiving a nursing intervention as Infection Protection also had Teaching: Procedure/Treatment, Skin Surveillance, Energy Management, Anxiety Reduction, Nutrition Management, Bowel Management, or Urinary Retention Care. Patients receiving the nursing intervention Nausea Management also had an additional intervention: Anxiety Reduction, Ventilation Assistance, Bowel Management, or Urinary Retention Care. Patients receiving the nursing intervention Teaching: Procedure/Treatment had an additional nursing intervention. They are Skin Surveillance, Energy Management Temperature Regulation, Anxiety Reduction, Nutrition Management, or Temperature Regulation. Patients receiving the nursing intervention Skin Surveillance also had an additional intervention: Anxiety Reduction, Bowel

100 83 Management, Fluid Management, or Temperature Regulation. Patients receiving the nursing intervention Energy Management also had Anxiety Reduction, Nutrition Management, Ventilation Assistance, Bowel Management, Fluid Management, Urinary Retention Care, Ventilation Assistance, or Temperature Regulation. Patients receiving the nursing intervention Anxiety Reduction also had Nutrition Management, Ventilation Assistance, or Oral Health Restoration. Patients receiving the nursing intervention Nutrition Management also had Bowel Management, Fluid Management, or Temperature Regulation. Patients receiving the nursing intervention Bowel Management also had Urinary Retention Care, or Temperature Regulation. Patients receiving the nursing intervention Fluid Management also had Temperature Regulation in this study sample. Research Question Five: What patient characteristics are associated with positive, no change, or negative nursing-sensitive patient outcome change scores at discharge associated with patient s most common nursing diagnoses? The study hospital selected several outcome indicators for each NOC outcome. Nurses document an overall rating from one to five based on these selected indicators. Outcome Change Scores for Pain Level To answer Question Five, a descriptive analysis for both Pain Level and Infection Severity for all units and by unit and a chi-square test for Pain Level by unit were performed. The outcome Pain Level was selected because it was associated with the primary nursing diagnosis selected for oncology patients in the study. Infection Severity was the most common NOC outcome in Adult Leukemia and Bone Marrow Transplant (Unit A). An outcome change score is calculated from two outcome rating scores (an

101 84 outcome change score = outcome rating at discharge a baseline outcome rating at admission). Outcome change scores require two scores. In this study, outcome ratings at admission and at discharge were selected to examine outcome change scores. The calculation of an outcome change score requires that a patient must have at least two outcome ratings. Since the NOC scores are from one to five, an outcome change scores may range from negative four to positive four. Finally, six categories of outcome change scores were used ( -3, -2, -1, 0, 1, 2). There were two collapsed categories due to few observations in those categories. Table 27 shows the distribution of outcomes change scores for Pain Level in patients with the diagnosis of Acute Pain by different characteristics of patients: gender, age, treatment and LOS. A total of 1,057 patients had at least two NOC outcome ratings for Pain Level. Overall, the majority of change scores of patients either remained the same (n = 415, 35%), or had a +1 change score (n=366, 35%). There were 11% of patients (n=118) had a +2 change score and 3% had a +3 or higher change scores (n=2.93). There were 10% of patients (n=105) that had a -1 outcome change score and 2 % (n=22) that had a -2 or lower change score. Outcome change scores for the majority of females and males remained the same score or have +1 increase in rating. Generally, the Caucasians had full range of change scores from -4 to +4. Specifically, 35% (n=366) of them remained the same, 30% (n=316) had +1 outcome change score improvement, and 10% (n=101) had +2 outcome change score improvement. The majority of African Americans had no change (n=23, 2%) or a +1 change score improvement (n=9, 1%).

102 85 Adults between 18 years and 65 years old remained the same or had a +1 outcome change score improvement (27 % for each). Adults older than 65 years old frequently had the same outcome scores and positive outcome change scores (20% in total). For all recorded Pain Level and treatments for cancer, there were no outcomes documented less than negative two. There were 60% surgical patients had either no change or had positive change scores for Pain Level. There were only four patients under mixed treatments and they had either a negative one or remained the same change scores. There were four patients under radiotherapy and four had positive one outcome scores. Three had negative one outcome change score for mix treatment and only one remained the same. For patients who stayed less than 24 hours, they seemed to have less outcome score documented. A smaller percentage of those patients had apparent improvement of outcome change scores in this group. The outcome change scores for this group who stayed less than 24 hours ranges from <= -2 to +1. Majority of patients had outcome scores located with the same score or positive one. There were 23% patients with LOS more than one day and less than the average LOS (M=3.7) that had outcome scores remain the same outcome scores. Figure 4 shows the distribution of outcomes change scores for Pain Level. It shows nearly half of patients (49%) discharged from the four oncology units had less pain, 39% had no change in Pain Level outcomes change scores, and 12% reported more pain than at admission. Most patients who reported less pain had a change score of a positive one (35%). Most patients discharged from Unit G, Unit H, and Unit A reported no change in the NOC outcome, Pain Level. Unit M is the only unit where the percentage of patients had a positive change score was higher than that of patients retain the same

103 86 outcome ratings. The most patients in the other three units, Unit G, Unit H, and Unit A had no change in the scores based on the difference between the first rating and last rating in the hospitalization. Unlike the histograms for Unit G, Unit H, and Unit M, the one for Unit A had a greater spread in these six categories of change scores, which means their patients, were more evenly distributed to each category. Unit H, Unit M, and Unit A show less spread, or a higher percentage of patients discharged from these three units were located in the categories of either the same or have +1 in change scores. There may be a potential bias due to its smaller sample of unit A than other three units. Outcome Change Scores for Pain Level Table 27 and Figure 5 show outcome change scores for patients who had the NOC outcome, Infection Severity. There were 42% of patients that had less severity at discharge for the NOC outcome, Infection Severity, 38% had no change and 20% infection became more severe at discharge for all units. Figure 5 provides a visual tool to explore the distribution of frequencies of outcome change scores for all units and for each. Instead of the number of patients who are grouped in six categories for different outcome change scores, the distribution of percentages for each unit was used to compare the improvement of a NOC outcome. This is because the number of patients differs hugely from a unit to another unit. Therefore, the percentage of outcome change scores was used for a unit comparison. Compared to the distribution of percentage for each unit itself, both Unit M and Unit A reported a higher percentage of patients had less pain at discharge. Unit M had higher percentage (56%) of discharge patients reporting less severity of infection, and followed by Unit H (40%), Unit A had (37%) and Unit G (31%). Unit G had 47% of

104 87 patients who had not changed in their severity of infection as the highest percentage of No Change in four units, Unit H had 41%, Unit M had had 30%, and unit M had 28%. In Unit M, there is twice of the number of patients who had decrease their infection severity than patients who had a greater degree of no change. Regarding the ranking of percentage of patients with more severity of infection at discharge, Unit A was the top, followed by Unit H (3.2%), Unit M (3.8), and Unit G (3.2%). Based on the percentage of patients that had infection severity at discharge, Unit A is in the top rank (33%), followed by Unit A (3.8), Unit M (4.2%) and Unit G (3.2%). Unit M had a higher percentage of patients with much less degree of infection severity and the lowest percentage of patients with more infection severity at discharge. Therefore, generally, Unit M had better performance in outcome change scores in NOC outcome, Infection Severity that relevant to NANDA-I diagnosis of Risk for infection. Unit A, compared to other three units, had higher percentage (6.7% and 23.3%, respectively) in the extreme positive ( 3) and negative outcome change scores (-2). In general, on a basis of outcome change scores for Pain Level and Infection Severity, most patients in Unit G tended to have no change in their scores at discharge, compared to the status at admission. Unit M had a higher percentage of patients that improved their pain and decreased the severity of infection scores than the other three units. Unit A had a lower percentage of patients showing improvements and a higher percentage of worsening situations at discharge than the other three units in most cases. Additionally, the histogram for Unit A shows much less discrepancy of percentages of change scores among the six categories of outcome change scores for Pain Level and it also had a likely W-shaped histogram for Infection Severity. Summary The study sample was on average 55 years old, mainly Caucasian, married, retired, and had private insurance coverage. The majority of the inpatients were newly

105 88 diagnosed with cancer or undergoing investigation. Most of their risk of mortality (80%) was mild to moderate. Nearly four fifths returned to their home with self-care and 4% of them expired in hospital. Patients in Unit A tend to have longer LOSs than the rest of the three units. Patients with combined SP, RT and CT had longer LOS than the patients who received other treatments. Except for the Unit A whose top ranked nursing diagnosis was Risk for Infection, Acute Pain was reported as the most frequent nursing diagnosis in the other three units. The most frequent linkages of NNN was Acute Pain Pain Management Pain Level. For most patients diagnosed with Acute Pain, their percentages of NOC outcome change scores in six categories ( -2, -1, 0, 1, 2, 3) were not changed by gender, age group, race, treatment, and type of insurance. However, from the histograms for distribution of two NOC outcomes, Pain Level and Infection Severity, their distribution of change scores portrayed in different shapes by unit. Patients in the Unit A tend to have less improvement in their change scores for the outcome Infection Severity compared to the other three units. The final chapter will discuss the results of this study and suggest implications for practice, education and research

106 89 Figure 4 NOC Outcome Change Scores for Pain All Units Unit G Unit H Unit M Unit A Note. n=1,057

107 90 Figure 5 NOC Outcome Change Scores for Infection Severity All Units Unit G Unit H Unit M Unit A Note. n=353

108 91 Table 1 Demographics, Disease Information, Hospitalization Information, and Type of Insurance Demographics n Mean ± SD (Range) Age (years) 2, ± 16.9 (18 99) n % Gender Female 1, Male Race Caucasian 1, African American 86 4 Asian 16 1 American Indian 12 1 Other Marital status Married 1, Life partner 7 <1 Divorced Separated 53 2 Single Widowed Education < 12 years years education < 16 years years education < 20 years years Employment Retired Unemployed Disabled Service Homemaker Administrator/Manager Student 77 3 Nurse and Physician 40 2 Other

109 92 Table 1 continued Disease Information Mean ± SD n (Range) Duration since first cancer diagnosis (month) ± 14.2 (0 215) n % Primary site of tumor Head and Neck Sites Thyroid Tongue Larynx Mouth Oropharynx Parotid Hypopharynx Paranasal Sinus/Nasal Cavity Lip and Oral Cavity Gum Pharynx Digestive System Pancreas Liver Stomach Large Intestine Rectum Peritoneum Extra-hepatic Bile Duct Esophagus Small Intestine GI Tract Thorax Lung Trachea/Pleura/Mediastinum Musculoskeletal Soft Tissues/Retroperitoneum Bone and Joints Skin Breast Central Nervous System Benign Brain Brain/Meninges/Spinal Cord

110 93 Table 1 Continued Gynecologic Sites Ovary Uterine leiomyoma Vulva Cervix Genitourinary Sites Renal Pelvis/Ureter/Other Kidney Urinary Bladder Prostate Testis Adrenal Penis Ophthalmic Sites Malign. Melanoma Ophthalmic Eye Lymphomas Non-Hodgkins Lymphoma Lymph Nodes Leukemia/Other Leukemia Myelo Disorders/Other Neuroendocrine Other Sites Benign Carcinoma in Situ Metastasis Unknown Primary/Other Neoplasm of Uncertain Behavior V-History of CA Under investigation for cancer diagnosis Pathologic Tumor Stage Stage Stage I Stage II 42 8 Stage III Stage IV Recurred, unstaged, unknown, or stage X Not applicable 83 15

111 94 Table 1 Continued Severity of Illness Minor Moderate Major Extreme Risk of Mortality Minor 1, Moderate Major Extreme Hospitalization Information Mean ± SD (Range) n Length of stay (LOS) in oncology unit 2, ± 4.6 (0 63) n % Discharge Units Gynecology, Oral Surgery, and Otolaryngology 1, Hematology/Oncology and Palliative Care Medical Surgical Oncology Adult Leukemia and Bone Marrow Transplant 45 2 Discharge status Home or self-care 1, Deceased 98 4 Home with home health care 84 4 Other facilities Treatment Surgery Chemotherapy Radiotherapy 11 2 Surgery and Radiotherapy 32 6 Chemotherapy and Radiotherapy 51 9 Surgery, Chemotherapy, and Radiotherapy Others

112 95 Table 1 Continued Cost-Related Variable n % Primary Insurance Medicaid Medicare Self-pay/uninsured 21 1 Private insurance 1, Blue Cross Blue Shield Commercial Insurance Local Insurance Company Veterans Administration, special program, worker 42 2 compensation, Tricare Note. Percentages may not sum to 100% due to rounding. 0 day for LOS in the study is defined as LOS less than 24 hour after admission (the exact minimum LOS in oncology units was 0.01 day), 0 day for duration of a cancer diagnosis in the study is defined as a patient was diagnosed in the specific visit. Carcinoma in situ is an early form of cancer that is defined by the absence of invasion of tumor cells into the surrounding tissue, usually before penetration through the basement membrane. The term "neoplasm of uncertain behavior" is a specific pathologic diagnosis. This is a lesion whose behavior cannot be predicted. It's currently benign, but there's a chance that it could undergo malignant transformation over time.

113 96 Table 2 Length of Stay (LOS) and Its Relationship with Type of Unit, Age Group, Race, Medical Treatment Group, and Type of Insurance Variable N M ± SD Range p Overall 2, ± Unit <.0001 Unit G 1, ± Unit H ± Unit M ± Unit A ± Treatment group <.0001 SP ± CT ± RT ± RT & CT ± SP, RT & CT ± Others ± Age group age<65 1, ± ± Race.576 White 1, ± African American ± Asian ± American Indian ± Other/unknown/denied ± Type of Insurance.001 Medicare ± Medicaid ± Self-pay/uninsured ± Private Insurance 1, ± Note. SP=Surgery procedures; CT=Chemotherapy; RT=Radiotherapy; Others=Hormone therapy, immunotherapy or other treatments that are not specified Unit G=Gynecology, Oral Surgery, and Otolaryngology Unit; Unit H=Hematology/Oncology and Palliative Care Unit Unit M=Medical Surgical Oncology Unit Unit A=Adult Leukemia and Bone Marrow Transplant Unit

114 97 Table 3 Count of Nursing Diagnoses (NANDA-I), Nursing Interventions (NIC), and Nursing-Sensitive Patient Outcomes (NOC) Per Person (N=2,237) Variable Mean ± SD Range Count of all nursing diagnoses (NANDA-I) 3.1 ± Count of all nursing interventions (NIC) 6.3 ± Count of all nursing sensitive patient outcomes (NOC) 3.7 ± Frequencies of NOC outcome ratings 9.3 ±

115 98 Table 4 Domains and Classes of the Unique 88 Nursing Diagnoses (NANDA-I) Domain: Activity/Rest Class (n=5) NANDA-I (n=14); Modified NANDA-I (n=6) n % 1 % 2 # Activity/Exercise Impaired Physical Mobility Impaired Bed Mobility Cardiovascular/Pulmonary Activity Intolerance Responses Ineffective Tissue Perfusion: Pulmonary b Ineffective Breathing Pattern Ineffective Tissue Perfusion, Cerebral b Ineffective Tissue Perfusion: Cardiac b Decreased Cardiac Output Impaired Spontaneous Ventilation Risk for Activity Intolerance Ineffective Tissue Perfusion, Renal b Ineffective Tissue Perfusion b Ineffective Tissue Perfusion, Peripheral Dysfunctional Ventilatory Weaning Response Energy Balance Fatigue Self-Care Self-Care Deficit a Bathing/Hygiene Self-Care Deficit Feeding: Self-Care Deficit Toileting Self-Care Deficit Sleep/Rest Sleep Deprivation Domain: Safety/Protection Class (n=5) NANDA-I (n=14); Modified NANDA-I (n=1) n % 1 % 2 # Defensive Process Risk for Latex Allergy Response Infection Risk for Infection

116 99 Table 4 Continued Physical Injury Impaired Skin Integrity Risk for Falls Ineffective Airway Clearance Risk for Impaired Skin Integrity Impaired Oral Mucous Membrane Risk for Bleeding Risk for Aspiration Risk for Injury Risk for Peripheral Neurovascular Dysfunction Disturbed Sensory Perception, Auditory b Thermoregulation Risk for Imbalanced Body Temperature Violence Risk for Suicide Risk for Self-Directed Violence Domain: Perception/Cognition Class (n=4) NANDA-I (n=7); Modified NANDA-I (n=6) n % 1 % 2 # Attention Unilateral Neglect Cognition Deficient Knowledge Pre/Post Procedure/Surgery a Communication Deficient Knowledge, Disease Process a Deficient Knowledge Acute Confusion Disturbed Thought Processes b Deficient Knowledge, Insulin Therapy a Impaired Memory Chronic Confusion Impaired Verbal Communication

117 100 Table 4 Continued Sensation/Perception Disturbed Sensory Perception, Visual b Disturbed Sensory Perception, Kinesthetic b Sensation/Perception Impaired Social Interaction Domain: Coping/Stress Tolerance Class (n=2) NANDA-I (n=9) n % 1 % 2 # Coping Responses Anxiety Grieving Readiness for Enhanced Family Coping Disabled Family Coping Ineffective Coping Readiness for Enhanced Coping Neurobehavioral Stress Decreased Intracranial Adaptive Capacity Autonomic Dysreflexia Risk for Autonomic Dysreflexia Domain: Elimination and Exchange Class (n=3) NANDA-I (n=7) n % 1 % 2 # Gastrointestinal Function Risk for Constipation Diarrhea Constipation Bowel Incontinence Respiratory Function Impaired Gas Exchange Urinary Function Impaired Urinary Elimination Urinary Retention Domain: Nutrition Class (n=3) NANDA-I (n=7) n % 1 % 2 # Hydration Risk for Imbalanced Fluid Volume Risk for Deficient Fluid Volume Deficient Fluid Volume Excess Fluid Volume

118 101 Table 4 Continued Ingestion Metabolism Imbalanced Nutrition: Less than Body Requirements Impaired Swallowing Risk for Unstable Blood Glucose Domain: Comfort Class (n=2) NANDA-I (n=3) n % 1 % 2 # Physical Comfort Nausea Chronic Pain Social Comfort Social Isolation Domain: Life Principles Class (n=1) NANDA-I (n=3) n % 1 % 2 # Beliefs Spiritual Distress Noncompliance Readiness for Enhanced Spiritual Well-Being Domain: Role Relationships Class (n=2) NANDA-I (n=2) n % 1 % 2 # Family Relationships Interrupted Family Process Domain: Self-Perception Class (n=2) NANDA-I (n=2) n % 1 % 2 # Body Image Disturbed Body Image Self-Esteem Situational Low Self-Esteem Domain: Growth/Development Class (n=1) NANDA-I (n=1) n % 1 % 2 # Development Delayed Growth and Development Domain: Health Promotion Class (n=1) NANDA-I (n=1) n % 1 % 2 # Health Management Ineffective Health Maintenance

119 102 Table 4 Continued Non-NANDA-I Diagnoses Nursing diagnoses n % 1 % 2 # Risk for Withdrawal: Alcohol/Drugs c Impaired Tissue Integrity c Mood Alteration: Depression c Mood Alteration: Mania c Inadequate Oral Food Beverage Intake c Note. % 1 : denominator is total nursing diagnoses; % 2 : denominator is total patient with this nursing diagnosis # : ranking of nursing diagnoses using % 1 for all unit Nursing diagnoses are modified global nursing diagnoses a, retired NANDA-I nursing diagnoses b or not recognized nursing diagnoses c by NANDA-I. Resource: Herdman, T. H. (Ed.). (2012). NANDA International nursing diagnoses: Definitions and classification Chichester, U.K: Wiley-Blackwell.

120 103 Table 5 Ranking of Nursing Diagnoses (NANDA-I) for All Units # NANDA-I n % 1 % 2 1 Acute Pain 1, Risk for Infection Nausea Impaired Skin Integrity Risk for Falls Deficient Knowledge Pre/Post Procedure/Surgery a Activity Intolerance Deficient Knowledge, Disease Process a Anxiety Imbalanced Nutrition: Less than Body Requirements Risk for Constipation Ineffective Airway Clearance Impaired Gas Exchange Risk for Impaired Skin Integrity Impaired Physical Mobility Fatigue Ineffective Tissue Perfusion: Pulmonary b Chronic Pain Risk for Imbalanced Fluid Volume Urinary Retention Impaired Oral Mucous Membrane Grieving Risk for Deficient Fluid Volume Ineffective Breathing Pattern Risk for Bleeding Deficient Knowledge Risk for Aspiration Risk for Imbalanced Body Temperature Acute Confusion Sleep Deprivation Deficient Fluid Volume Diarrhea Excess Fluid Volume Readiness for Enhanced Family Coping Ineffective Tissue Perfusion, Cerebral b Self-Care Deficit a

121 104 Table 5 Continued 37 Impaired Swallowing Decreased Intracranial Adaptive Capacity Ineffective Tissue Perfusion: Cardiac b Spiritual Distress Bathing/Hygiene Self-Care Deficit Constipation Decreased Cardiac Output Disturbed Thought Processes b Impaired Verbal Communication Impaired Spontaneous Ventilation Risk for Activity Intolerance Impaired Bed Mobility Feeding: Self-Care Deficit Ineffective Tissue Perfusion, Renal b Risk for Unstable Blood Glucose Disturbed Body Image Risk for Injury Risk for Peripheral Neurovascular Dysfunction Risk for Withdrawal: Alcohol/Drugs c Deficient Knowledge, Insulin Therapy a Disabled Family Coping Ineffective Tissue Perfusion a Noncompliance Readiness for Enhanced Spiritual Well-Being Impaired Memory Ineffective Coping Unilateral Neglect Impaired Urinary Elimination Ineffective Tissue Perfusion, Peripheral Social Isolation Disturbed Sensory Perception, Visual b Risk for Latex Allergy Response Bowel Incontinence Chronic Confusion Ineffective Health Maintenance Disturbed Sensory Perception, Kinesthetic b

122 105 Table 5 Continued 73 Disturbed Sensory Perception, Auditory b Dysfunctional Ventilatory Weaning Response Impaired Social Interaction Impaired Tissue Integrity Mood Alteration: Depression c Risk for Suicide Autonomic Dysreflexia Delayed Growth and Development Inadequate Oral Food Beverage Intake c Interrupted Family Process Mood Alteration: Mania c Readiness for Enhanced Coping Risk for Autonomic Dysreflexia Risk for Self-Directed Violence Situational Low Self-Esteem Toileting Self-Care Deficit Note. (Nursing Diagnoses, N=7,002; Patients, N=2,237) % 1 : denominator is total nursing diagnoses; % 2 : denominator is total patient with this nursing diagnosis Nursing diagnoses are modified global nursing diagnoses a, retired NANDA-I nursing diagnoses b or not recognized nursing diagnoses c by NANDA-I.

123 106 Table 6 Domain and Class of the Nursing Interventions (NIC) Domain: Behavioral Class (n=6) NIC (n=33) n 1 % 1 n 2 % 2 # Behavior Therapy Activity Therapy Substance Use Treatment: Alcohol Withdrawal Mutual Goal Setting Self-Responsibility Facilitation Substance Use Treatment: Drug Withdrawal Behavior Management: Self-Harm Mechanical Ventilatory Weaning Impulse Control Training Cognitive Therapy Cognitive Stimulation Cognitive Restructuring Seizure Precaution Memory Training Reality Orientation Communication Communication Enhancement Enhancement: Speech Deficit Active Listening Socialization Enhancement Communication Enhancement: Hearing Deficit Communication Enhancement: Visual Deficit Coping Assistance Coping Enhancement Grief Work Facilitation Emotional Support Dying Care Spiritual Support Spiritual Growth Facilitation

124 107 Table 6 Continued Patient Education Psychological Comfort Promotion Body Image Enhancement Self-Esteem Enhancement Mood Management Teaching: Procedure/Treatment Teaching: Preoperative Teaching: Disease Process Teaching: Individual Health Education Anxiety Reduction Domain: Physiological: Complex Class (n=6) NIC (n=27) n 1 % 1 n 2 % 2 # Drug Management Analgesic Administration Electrolyte and Acid-Base Management: Acid-Base Management Respiratory Acidosis Acid-Base Management Hyperglycemia Management Hypoglycemia Management Neurologic Neurologic Monitoring Management Cerebral Perfusion Promotion Cerebral Edema Management Peripheral Sensation Management Unilateral Neglect Management Seizure Management Dysreflexia Management Respiratory Ventilation Assistance Management Airway Management

125 108 Table 6 Continued Skin/Wound Management Tissue Perfusion Management Airway Suctioning Aspiration Precautions Artificial Airway Management Skin Surveillance Wound Care Pressure Ulcer Care Fluid Management Fluid Monitoring Temperature Regulation Bleeding Precaution Cardiac Care: Acute Circulatory Care: Venous Insufficiency Circulatory Care: Arterial Insufficiency Domain: Physiological: Basic Class (n=6) NIC (n=26) n 1 % 1 n 2 % 2 # Activity and Exercise Management Elimination Management Immobility Management Energy Management Exercise Promotion: Strength Training Exercise Promotion Exercise Therapy: Balance Bowel Management Urinary Retention Care Diarrhea Management Constipation/Impaction Management Urinary Habit Training Bowel Incontinence Care Positioning

126 109 Table 6 Continued Nutrition Support Nutrition Management Nutrition Therapy Diet Staging Nutritional Monitoring Nutrition Education a Physical Comfort Pain Management 2, , Support Nausea Management Self-Care Oral Health Restoration Facilitation Self-Care Assistance Sleep Enhancement Self-Care Assistance: Feeding Foot Care Self-Care Assistance: Toileting Self-Care Assistance: Bathing/Hygiene Self-Care Assistance: Dressing/grooming Domain: Safety Class ((n=2) NIC (n=11) n 1 % 1 n 2 % 2 # Crisis Suicide Prevention Management Risk Management Fall Prevention Infection Protection Infection Control Pressure Management Delirium Management Environmental Management Latex Precautions Hallucination Management Surveillance: Safety Health Screening

127 110 Table 6 Continued Domain: Family Class (n=1) NIC (n=3) n 1 % 1 n 2 % 2 # Lifespan Care Family Support Family Therapy Family Process Maintenance Note. n 1 =Count all interventions applied to each diagnoses in independent patient (N=2,337) including duplicate interventions linked to different diagnoses. n 2 =Count any of 105 interventions presented in individual patient, excluding duplicate interventions. % 1 (a sum of nursing interventions on the basis of all units or cumulative frequencies of any time of nursing interventions have been documented even though it can be on the same patients) were calculated twice, while % 2 (a nursing intervention that individual had received, and any documented duplicated nursing interventions would be counted as one)was calculated once. # is the ranking of nursing interventions using % for all units Nutrition Education a is a modified nursing intervention by the study hospital, which is not NIC intervention.

128 111 Table 7 Ranking of NIC Nursing Interventions for All Units Interventions for all units Patient with interventions # NIC n 1 % 1 n 2 % 2 1 Pain Management 2, , Fall Prevention Infection Protection Infection Control Nausea Management Teaching: Procedure/Treatment Analgesic Administration Skin Surveillance Wound Care Pressure Management Energy Management Teaching: Preoperative Activity Therapy Exercise Promotion: Strength Training Anxiety Reduction Nutrition Management Nutrition Therapy Ventilation Assistance Teaching: Disease Process Bowel Management Diet Staging Airway Management Airway Suctioning Acid-Base Management: Respiratory Acidosis Acid-Base Management Fluid Management Exercise Promotion Fluid Monitoring Aspiration Precautions Urinary Retention Care Temperature Regulation Coping Enhancement Oral Health Restoration Grief Work Facilitation

129 112 Table 7 Continued 35 Self-Care Assistance Bleeding Precaution Neurologic Monitoring Nutritional Monitoring Teaching: Individual Diarrhea Management Delirium Management Health Education Cardiac Care: Acute Emotional Support Circulatory Care: Venous Insufficiency Circulatory Care: Arterial Insufficiency Sleep Enhancement Cognitive Stimulation Cognitive Restructuring Family Support Pressure Ulcer Care Cerebral Perfusion Promotion Cerebral Edema Management Dying Care Positioning Constipation/Impaction Management Seizure Precaution Spiritual Support Communication Enhancement: Speech Deficit Artificial Airway Management Environmental Management Active Listening Substance Use Treatment: Alcohol Withdrawal Hyperglycemia Management Hypoglycemia Management Peripheral Sensation Management Spiritual Growth Facilitation Body Image Enhancement Mutual Goal Setting Socialization Enhancement Family Therapy

130 113 Table 7 Continued 72 Memory Training Reality Orientation Self-Responsibility Facilitation Unilateral Neglect Management Substance Use Treatment: Drug Withdrawal Latex Precautions Urinary Habit Training Behavior Management: Self-Harm Self-Care Assistance: Feeding Communication Enhancement: Hearing Deficit Communication Enhancement: Visual Deficit Foot Care Hallucination Management Seizure Management Exercise Therapy: Balance Dysreflexia Management Impulse Control Training Mechanical Ventilatory Weaning Self-Care Assistance: Toileting Self-Esteem Enhancement Suicide Prevention Surveillance: Safety Bowel Incontinence Care Family Process Maintenance Health Screening Mood Management Nutrition Education Self-Care Assistance: Bathing/Hygiene Self-Care Assistance: Dressing/grooming Note. Nursing Interventions (N=11,804); Patients (N=2,237); n 1 =Count all interventions applied to each diagnoses in independent patient including duplicate interventions linked to different diagnoses. n 2 =Count any of 105 interventions presented in individual patient, excluding duplicate interventions. % 1 (a sum of nursing interventions on the basis of all units or cumulative frequencies of any time of nursing interventions have been documented even though it can be on the same patients) were calculated twice, while % 2 (a nursing intervention that individual had received, and any documented duplicated nursing interventions would be counted as one)was calculated once. # = ranking of nursing interventions using % for all units; Nutrition Education a is a modified nursing intervention by the study hospital, which is not NIC intervention.

131 114 Table 8 Domains and Classes of Nursing Outcomes (NOC) Domain: Perceived Health Class (n=8) NOC (n=37) n 1 % 1 n 2 % 2 # Cardiopulmonary Digestion & Nutrition Respiratory Status: Airway Patency Respiratory Status: Gas Exchange Tissue Perfusion: Pulmonary Blood Loss Severity Respiratory Status: Ventilation Tissue Perfusion: Cerebral Tissue Perfusion: Cardiac Tissue Perfusion: Peripheral Cardiac Pump Effectiveness Nutritional Status Gastrointestinal Function Swallowing Status Nutritional Status: Food and Fluid Intake Elimination Urinary Elimination Bowel Elimination Kidney Function Urinary Continence Fluid & Electrolytes Hydration Fluid Balance Fluid Overload Severity Immune Response Infection Severity Allergic Response: Systemic Neurocognitive Acute Confusion Level Cognitive Orientation Neurological Status Communication Cognition Neurological Status: Peripheral Heedfulness of Affected Side Memory Communication: Receptive Neurological Status: Autonomic

132 115 Table 8 Continued Sensory Function Sensory Function: Vision Tissue Integrity: Skin and Tissue Integrity Mucous Membranes Oral Hygiene Burn Healing Oral Intake Domain: Health Knowledge & Behavior Class (n=3) NOC (n=14) n 1 % 1 n 2 % 2 # Health Behavior Pain Control Seizure Control Compliance Behavior Diabetes Self-Management Health Seeking Behavior Health Knowledge Knowledge: Treatment Procedure Knowledge: Fall Prevention Knowledge: Illness Care Knowledge: Treatment Regimen Knowledge: Personal Safety Risk Control & Aspiration Prevention Safety Risk Control: Hyperthermia Risk Control: Hypothermia Risk Control Domain: Functional Health Class (n=6) NOC (n=11) n 1 % 1 n 2 % 2 # Energy Maintenance Activity Tolerance Endurance Sleep Growth & Growth Development Mobility Mobility Body Positioning: Self-Initiated Balance

133 116 Table 8 Continued Risk Control & Safety Therapeutic Response Fall Prevention: Behavior Self-Care: Activities of Daily Living(ADL) Self-Care Status Blood Glucose Level Domain: Psychosocial Health Class (n=4) NOC (n=11) n % n % # Psychological Well- Body Image Being Depression Level Mood Equilibrium Self-Esteem Psychosocial Coping Grief Resolution Dignified Life Closure Self-Control Suicide Self-Restraint Self-Mutilation Restraint Social Interaction Social Involvement Social Interaction Skills Domain: Perceived Health Class (n=2) NOC (n=5) n % n % # Health & Life Quality Spiritual Health Symptom Status Pain Level 1, , Nausea and Vomiting Severity Pain: Disruptive Effects Substance Withdrawal Severity Domain: Family Health Class (n=1) NOC (n=2) n % n % # Family Well-Being Family Coping Family Integrity

134 117 Note. n 1 refers to count all outcomes applied to each o in independent patients (N=2,337) including duplicate outcomes linked to different diagnoses. n 2 refers to count any of 81 outcomes presented individual patient, excluding duplicate outcomes. # refers to the ranking of outcomes using % for all units Oral Intake is a modified nursing-sensitive patient outcome used in the study hospital. This is not a recognized NOC outcome.

135 118 Table 9 Ranking of Nursing -Sensitive Patient Outcomes (NOC) for All Units Outcomes for all units Patient with Outcome # NOC n 1 % 1 n 2 % 2 1 Pain Level 1, , Infection Severity Nausea and Vomiting Severity Knowledge: Treatment Procedure Tissue Integrity: Skin and Mucous Membranes Pain Control Knowledge: Fall Prevention Fall Prevention: Behavior Activity Tolerance Anxiety Level Nutritional Status Knowledge: Illness Care Gastrointestinal Function Respiratory Status: Airway Patency Respiratory Status: Gas Exchange Mobility Endurance Tissue Perfusion: Pulmonary Hydration Fluid Balance Urinary Elimination Aspiration Prevention Coping Oral Hygiene Grief Resolution Self-Care: Activities of Daily Living(ADL) Bowel Elimination Blood Loss Severity Respiratory Status: Ventilation Knowledge: Treatment Regimen Family Coping Acute Confusion Level Pain: Disruptive Effects Risk Control: Hyperthermia Risk Control: Hypothermia

136 119 Table 9 Continued 36 Sleep Fluid Overload Severity Spiritual Health Tissue Perfusion: Cerebral Cognitive Orientation Neurological Status Swallowing Status Dignified Life Closure Tissue Perfusion: Cardiac Tissue Perfusion: Peripheral Cardiac Pump Effectiveness Communication Seizure Control Cognition Blood Glucose Level Body Positioning: Self-Initiated Kidney Function Burn Healing Neurological Status: Peripheral Substance Withdrawal Severity Body Image Compliance Behavior Diabetes Self-Management Knowledge: Personal Safety Heedfulness of Affected Side Memory Allergic Response: Systemic Sensory Function: Vision Urinary Continence Nutritional Status: Food and Fluid Intake Social Involvement Health Seeking Behavior Social Interaction Skills Balance Communication: Receptive Depression Level Neurological Status: Autonomic

137 120 Table 9 Continued 73 Risk Control Self-Care Status Suicide Self-Restraint Family Integrity Growth Mood Equilibrium Oral Intake Self-Esteem Self-Mutilation Restraint Note. n 1 = Count all outcomes applied to each diagnoses in independent patients including duplicate outcomes linked to different diagnoses. n 2 = Count any of 81 outcomes presented individual patient, excluding duplicate outcomes. % 1 = the number of a specific NOC outcome divided by the total number of outcomes for all units (N=8,197). % 2 = the number of patient with a specific outcome divided by the total number of participants (N=2,337). Oral Intake is a modified nursing sensitive patient outcome used in the study hospital. This is not a recognized NOC outcome.

138 121 Table 10 Ranking of NANDA-I (Nursing Diagnoses) by Unit Unit G Unit H Unit M Unit A NANDA-I n % n % n % n % p Acute Pain* <.0001 Risk for Infection* <.0001 Nausea* <.0001 Impaired Skin Integrity* <.0001 Risk for Falls* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery* <.0001 Activity Intolerance* Deficient Knowledge, Disease Process* <.0001 Anxiety* <.0001 Imbalanced Nutrition: Less than Body Requirements* <.0001 Risk for Constipation* <.0001 Ineffective Airway <.0001 Clearance* Impaired Gas Exchange* <.0001 Risk for Impaired Skin Integrity Impaired Physical Mobility Fatigue* <.0001 Ineffective Tissue Perfusion: Pulmonary* <.0001 Chronic Pain* <.0001 Risk for Imbalanced Fluid Volume Urinary Retention* <.0001 Impaired Oral Mucous Membrane* <.0001 Grieving* <.0001 Risk for Deficient Fluid Volume* <.0001 Ineffective Breathing Pattern* Risk for Bleeding* Deficient Knowledge* <.0001

139 122 Table 10 Continued Risk for Aspiration Risk for Imbalanced Body Temperature Acute Confusion* <.0001 Sleep Deprivation Deficient Fluid Volume Diarrhea* <.0001 Excess Fluid Volume* <.0001 Readiness for Enhanced <.0001 Family Coping* Ineffective Tissue Perfusion, Cerebral* <.0001 Self-Care Deficit* <.0001 Impaired Swallowing Decreased Intracranial Adaptive Capacity Ineffective Tissue Perfusion: Cardiac Spiritual Distress Bathing/Hygiene Self-Care Deficit Constipation Decreased Cardiac Output Disturbed Thought Processes Impaired Verbal Communication Impaired Spontaneous Ventilation Risk for Activity Intolerance Impaired Bed Mobility Feeding: Self-Care Deficit Ineffective Tissue Perfusion, Renal Risk for Unstable Blood Glucose Disturbed Body Image

140 123 Table 10 Continued Risk for Injury Risk for Peripheral Neurovascular Dysfunction Risk for Withdrawal: Alcohol/Drugs Deficient Knowledge, Insulin Therapy Disabled Family Coping Ineffective Tissue Perfusion Noncompliance Readiness for Enhanced Spiritual Well-Being Impaired Memory Ineffective Coping Unilateral Neglect Impaired Urinary Elimination Ineffective Tissue Perfusion, Peripheral Social Isolation Disturbed Sensory Perception, Visual Risk for Latex Allergy Response Bowel Incontinence Chronic Confusion Ineffective Health Maintenance Disturbed Sensory Perception, Kinesthetic Disturbed Sensory Perception, Auditory Dysfunctional Ventilatory Weaning Response Impaired Social Interaction Impaired Tissue Integrity Mood Alteration: Depression

141 124 Table 10 Continued Risk for Suicide Autonomic Dysreflexia Delayed Growth and Development Inadequate Oral Food Beverage Intake Interrupted Family Process Mood Alteration: Mania Readiness for Enhanced Coping Risk for Autonomic Dysreflexia Risk for Self-Directed Violence Situational Low Self Esteem Toileting Self-Care Deficit Note. Unit G = Gynecology, Oral Surgery, and Otolaryngology Unit; Unit H = Hematology/Oncology and Palliative Care Unit; Unit M = Medical Surgical Oncology Unit; Unit A = Adult Leukemia and Bone Marrow Transplant Unit *= significant findings at α =.05, p value = was adjusted from p =.05 after Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=88).

142 125 Table 11 Ranking of Nursing Diagnoses (NANDA-I) Found Significantly Different by Unit # Unit G Unit H Unit M Unit A 1 Acute Pain Acute Pain Acute Pain Risk for Infection 2 Nausea Risk for Infection Risk for Infection Nausea 3 Risk for Infection Risk for Falls Impaired Skin Integrity 4 Deficient Knowledge Pre/Post Procedure/Surgery 5 Impaired Skin Integrity Deficient Knowledge, Disease Process Nausea Risk for Falls Acute Pain Deficient Knowledge, Disease Process Nausea 6 Risk for Falls Anxiety Deficient Knowledge Pre/Post Procedure/Surgery 7 Activity Intolerance 8 Risk for Constipation Ineffective Airway Clearance 9 Anxiety Impaired Gas Exchange 10 Imbalanced Nutrition: Less than Body Requirements 11 Ineffective Airway Clearance Risk for Falls Fatigue Activity Intolerance Impaired Oral Mucous Membrane Activity Intolerance Imbalanced Nutrition: Less than Body Requirements Imbalanced Nutrition: Less than Body Requirements Ineffective Tissue Perfusion: Pulmonary Deficient Knowledge, Disease Process Anxiety Risk for Constipation Activity Intolerance Risk for Deficient Fluid Volume Anxiety Imbalanced Nutrition: Less than Body Requirements 12 Urinary Retention Grieving Chronic Pain Diarrhea 13 Impaired Gas Fatigue Fatigue Risk for Bleeding Exchange 14 Risk for Imbalanced Fluid Volume Impaired Skin Integrity Impaired Gas Exchange Deficient Knowledge

143 126 Table 11 Continued 15 Deficient Chronic Pain Knowledge, Disease Process 16 Risk for Bleeding Deficient Knowledge Pre/Post Procedure/Surgery 17 Ineffective Tissue Perfusion: Pulmonary 18 Ineffective Breathing Pattern Impaired Oral Mucous Membrane Readiness for Enhanced Family Coping Risk for Imbalanced Fluid Volume Ineffective Airway Clearance Ineffective Tissue Perfusion: Pulmonary Deficient Knowledge Self-Care Deficit Deficient Knowledge Pre/Post Procedure/Surgery Impaired Gas Exchange Chronic Pain 19 Risk for Aspiration Acute Confusion Risk for Bleeding Risk for Imbalanced Fluid Volume 20 Risk for Imbalanced Body Temperature 21 Deficient Knowledge Risk for Deficient Fluid Volume Ineffective Breathing Pattern 22 Chronic Pain Risk for Imbalanced Fluid Volume 23 Risk for Deficient Excess Fluid Fluid Volume Volume 24 Ineffective Tissue Ineffective Tissue Perfusion, Cerebral Perfusion, Cerebral Urinary Retention Risk for Imbalanced Body Temperature Risk for Deficient Fluid Volume Ineffective Airway Clearance Ineffective Tissue Perfusion: Pulmonary Ineffective Breathing Pattern Ineffective Breathing Pattern Impaired Skin Integrity Acute Confusion Risk for Aspiration 25 Fatigue Risk for Aspiration Diarrhea Acute Confusion 26 Impaired Oral Risk for Excess Fluid Excess Fluid Mucous Imbalanced Body Volume Volume Membrane Temperature 27 Acute Confusion Self-Care Deficit Grieving Ineffective Tissue Perfusion, Cerebral 28 Diarrhea Risk for Constipation Risk for Aspiration Risk for Constipation 29 Grieving Risk for Bleeding Impaired Oral Mucous Membrane Urinary Retention

144 127 Table 11 Continued 30 Readiness for Enhanced Family Coping 31 Self-Care Deficit Deficient Knowledge 32 Excess Fluid Volume Diarrhea Self-Care Deficit Grieving Urinary Retention Readiness for Enhanced Family Coping Ineffective Tissue Perfusion, Cerebral Risk for Imbalanced Body Temperature Readiness for Enhanced Family Coping Note. Unit G = Gynecology, Oral Surgery, and Otolaryngology Unit; Unit H = Hematology/Oncology and Palliative Care Unit; Unit M = Medical Surgical Oncology Unit; Unit A = Adult Leukemia and Bone Marrow Transplant Unit

145 128 Table 12 Ranking of Nursing Interventions (NIC) by Unit Unit G Unit H Unit M Unit A NIC n % n % n % n % p Pain Management* <.0001 Fall Prevention* <.0001 Infection Protection* <.0001 Infection Control* <.0001 Nausea Management* <.0001 Teaching: <.0001 Procedure/Treatment* Analgesic Administration* <.0001 Skin Surveillance* <.0001 Wound Care* <.0001 Pressure Management* <.0001 Energy Management* <.0001 Teaching: Preoperative* <.0001 Activity Therapy* <.0001 Exercise Promotion: Strength <.0001 Training* Anxiety Reduction* <.0001 Nutrition Management* <.0001 Nutrition Therapy* <.0001 Ventilation Assistance* <.0001 Teaching: Disease Process* <.0001 Bowel Management* <.0001 Diet Staging* <.0001 Airway Management* <.0001

146 129 Table 12 Continued Airway Suctioning* <.0001 Acid-Base Management: <.0001 Respiratory Acidosis* Acid-Base Management* <.0001 Fluid Management* <.0001 Exercise Promotion Fluid Monitoring Aspiration Precautions* Urinary Retention Care* <.0001 Temperature Regulation Coping Enhancement* <.0001 Oral Health Restoration* <.0001 Grief Work Facilitation* <.0001 Self-Care Assistance* <.0001 Bleeding Precaution* Neurologic Monitoring* <.0001 Nutritional Monitoring Teaching: Individual* <.0001 Diarrhea Management* <.0001 Delirium Management* <.0001 Health Education* <.0001 Cardiac Care: Acute* <.0001 Emotional Support* Circulatory Care: Venous <.0001 Insufficiency* Circulatory Care: Arterial Insufficiency* <.0001

147 130 Table 12 Continued Sleep Enhancement Cognitive Stimulation Cognitive Restructuring Family Support Pressure Ulcer Care Cerebral Perfusion Promotion Cerebral Edema Management Dying Care Positioning Constipation/Impaction Management Seizure Precaution Spiritual Support Communication Enhancement: Speech Deficit Artificial Airway Management Environmental Management Active Listening Substance Use Treatment: Alcohol Withdrawal Hyperglycemia Management Hypoglycemia Management Peripheral Sensation Management Spiritual Growth Facilitation Body Image Enhancement Mutual Goal Setting

148 131 Table 12 Continued Socialization Enhancement Family Therapy Memory Training Reality Orientation Self-Responsibility Facilitation Unilateral Neglect Management Substance Use Treatment: Drug Withdrawal Latex Precautions Urinary Habit Training Behavior Management: Self Harm Self-Care Assistance: Feeding Communication Enhancement: Hearing Deficit Communication Enhancement: Visual Deficit Foot Care Hallucination Management Seizure Management Exercise Therapy: Balance Dysreflexia Management Impulse Control Training Mechanical Ventilatory Weaning Self-Care Assistance: Toileting Self-Esteem Enhancement

149 132 Table 12 Continued Suicide Prevention Surveillance: Safety Bowel Incontinence Care Family Process Maintenance Health Screening Mood Management Nutrition Education Self-Care Assistance: Bathing/Hygiene Self-Care Assistance: Dressing/grooming Note. Unit G = Gynecology, Oral Surgery, and Otolaryngology Unit; Unit H = Hematology/Oncology and Palliative Care Unit Unit M = Medical Surgical Oncology Unit; Unit A = Adult Leukemia and Bone Marrow Transplant Unit *= significant findings at α =.05, p value =.0005 was adjusted from p =.05 after Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=100).

150 133 Table 13 Ranking of Nursing Interventions (NIC) Found Significantly Different by Unit # Unit G Unit H Unit M Unit A 1 Pain Management Pain Management Pain Management Infection Protection 2 Nausea Management Infection Protection Analgesic Administration Infection Control 3 Infection Protection Infection Control Infection Protection Teaching: Procedure/ Treatment 4 Infection Control Teaching: Procedure/ Infection Control Nausea Management Treatment 5 Teaching: Procedure/ Fall Prevention Skin Surveillance Pain Management Treatment 6 Skin Surveillance Analgesic Teaching: Procedure/ Energy Management Administration Treatment 7 Wound Care Nausea Management Wound Care Fall Prevention 8 Teaching: Preoperative Energy Management Pressure Management Oral Health Restoration 9 Pressure Management Ventilation Fall Prevention Activity Therapy Assistance 10 Analgesic Administration Anxiety Reduction Nausea Management Exercise Promotion: Strength Training 11 Fall Prevention Airway Management Energy Management Anxiety Reduction 12 Energy Management Activity Therapy Teaching: Preoperative Fluid Management 13 Activity Therapy Exercise Promotion: Exercise Promotion: Nutrition Management Strength Training Strength Training 14 Exercise Promotion: Strength Training Airway Suctioning Activity Therapy Nutrition Therapy

151 134 Table 13 Continued 15 Bowel Management Acid-Base Nutrition Management Self-Care Assistance Management: Respiratory Acidosis 16 Diet Staging Teaching: Disease Nutrition Therapy Diarrhea Management Process 17 Anxiety Reduction Skin Surveillance Teaching: Disease Process Analgesic Administration 18 Urinary Retention Care Nutrition Anxiety Reduction Bleeding Precaution Management 19 Airway Management Nutrition Therapy Bowel Management Teaching: Individual 20 Nutrition Management Acid-Base Diet Staging Health Education Management 21 Nutrition Therapy Fluid Management Ventilation Assistance Ventilation Assistance 22 Airway Suctioning Grief Work Fluid Management Teaching: Disease Process Facilitation 23 Ventilation Assistance Pressure Acid-Base Management: Skin Surveillance Management Respiratory Acidosis 24 Acid-Base Management: Wound Care Airway Management Airway Management Respiratory Acidosis 25 Teaching: Disease Process Oral Health Restoration Airway Suctioning Airway Suctioning 26 Acid-Base Management Coping Enhancement Nutritional Monitoring Acid-Base Management: Respiratory Acidosis 27 Aspiration Precautions Teaching: Acid-Base Management Acid-Base Management Preoperative 28 Bleeding Precaution Neurologic Teaching: Individual Coping Enhancement Monitoring 29 Coping Enhancement Aspiration Precautions Health Education Wound Care

152 135 Table 13 Continued 30 Fluid Management Delirium Diarrhea Management Pressure Management Management 31 Teaching: Individual Circulatory Care: Emotional Support Aspiration Precautions Venous Insufficiency 32 Health Education Circulatory Care: Bleeding Precaution Neurologic Monitoring Arterial Insufficiency 33 Nutritional Monitoring Self-Care Assistance Urinary Retention Care Nutritional Monitoring 34 Diarrhea Management Cardiac Care: Acute Coping Enhancement Delirium Management 35 Neurologic Monitoring Emotional Support Aspiration Precautions Cardiac Care: Acute 36 Self-Care Assistance Nutritional Monitoring Neurologic Monitoring Circulatory Care: Venous Insufficiency 37 Oral Health Restoration Diarrhea Management Delirium Management Circulatory Care: Arterial Insufficiency 38 Delirium Management Diet Staging Circulatory Care: Venous Teaching: Preoperative Insufficiency 39 Emotional Support Bowel Management Circulatory Care: Arterial Bowel Management Insufficiency 40 Circulatory Care: Venous Bleeding Precaution Grief Work Facilitation Diet Staging Insufficiency 41 Circulatory Care: Arterial Teaching: Individual Self-Care Assistance Urinary Retention Care Insufficiency 42 Grief Work Facilitation Health Education Cardiac Care: Acute Grief Work Facilitation 43 Cardiac Care: Acute Urinary Retention Care Oral Health Restoration Emotional Support Note. Unit G = Gynecology, Oral Surgery, and Otolaryngology Unit; Unit H = Hematology/Oncology and Palliative Care Unit; Unit M = Medical Surgical Oncology Unit; Unit A = Adult Leukemia and Bone Marrow Transplant Unit #=ranking

153 136 Table 14 Ranking of Nursing-Sensitive Patient Outcomes (NOC) by Unit Unit G Unit H Unit M Unit A NOC n % n % n % n % p Pain Level* <.0001 Infection Severity* <.0001 Nausea and Vomiting Severity* <.0001 Knowledge: Treatment Procedure* <.0001 Tissue Integrity: Skin and Mucous Membranes* <.0001 Pain Control* <.0001 Knowledge: Fall Prevention* <.0001 Fall Prevention: Behavior* <.0001 Activity Tolerance* <.0001 Anxiety Level* <.0001 Nutritional Status* <.0001 Knowledge: Illness Care* <.0001 Gastrointestinal Function* <.0001 Respiratory Status: Airway Patency* <.0001 Respiratory Status: Gas Exchange* <.0001 Mobility Endurance* <.0001 Tissue Perfusion: Pulmonary* <.0001 Hydration Fluid Balance* <.0001 Urinary Elimination* <.0001 Aspiration Prevention* <.0001 Coping* <.0001 Oral Hygiene* <.0001

154 137 Table 14 Continued Grief Resolution* <.0001 Self-Care: Activities of Daily Living(ADL)* <.0001 Bowel Elimination* <.0001 Blood Loss Severity* <.0001 Respiratory Status: Ventilation* <.0001 Knowledge: Treatment Regimen* <.0001 Family Coping* <.0001 Acute Confusion Level* <.0001 Pain: Disruptive Effects* <.0001 Risk Control: Hyperthermia Risk Control: Hypothermia Sleep Fluid Overload Severity* <.0001 Spiritual Health* <.0001 Tissue Perfusion: Cerebral* <.0001 Cognitive Orientation* Neurological Status* Swallowing Status* Dignified Life Closure* Tissue Perfusion: Cardiac* Tissue Perfusion: Peripheral Cardiac Pump Effectiveness* Communication Seizure Control* Cognition*

155 138 Table 14 Continued Blood Glucose Level Body Positioning: Self-Initiated Kidney Function Burn Healing Neurological Status: Peripheral* Substance Withdrawal Severity Body Image* Compliance Behavior Diabetes Self-Management Knowledge: Personal Safety Heedfulness of Affected Side* Memory Allergic Response: Systemic Sensory Function: Vision Urinary Continence Nutritional Status: Food and Fluid Intake Social Involvement Health Seeking Behavior Social Interaction Skills Balance Communication: Receptive Depression Level Neurological Status: Autonomic Risk Control Self-Care Status

156 139 Table 14 Continued Suicide Self-Restraint Family Integrity Growth Mood Equilibrium Oral Intake Self-Esteem Self-Mutilation Restraint Note. Unit G = Gynecology, Oral Surgery, and Otolaryngology Unit; Unit H = Hematology/Oncology and Palliative Care Unit; Unit M = Medical Surgical Oncology Unit; Unit A = Adult Leukemia and Bone Marrow Transplant Unit *=significant findings at α =.05, p value = was adjusted from p=.05 after Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=81).

157 140 Table 15 The Top Ranking of Nursing Outcomes (NOC) Found Significantly Different by Unit # Unit G Unit H Unit M Unit A 1 Pain Level Pain Level Pain Level Infection Severity 2 Nausea and Vomiting Severity Infection Severity Pain Control Knowledge: Treatment Procedure 3 Infection Severity Knowledge: Treatment Procedure Infection Severity Nausea and Vomiting Severity 4 Tissue Integrity: Skin and Knowledge: Fall Prevention Tissue Integrity: Skin and Pain Level Mucous Membranes Mucous Membranes 5 Knowledge: Treatment Fall Prevention: Behavior Knowledge: Treatment Knowledge: Fall Prevention Procedure Procedure 6 Pain Control Pain Control Fall Prevention: Behavior Fall Prevention: Behavior 7 Knowledge: Fall Prevention Nausea and Vomiting Severity Knowledge: Fall Prevention Endurance 8 Fall Prevention: Behavior Anxiety Level Nausea and Vomiting Severity Oral Hygiene 9 Activity Tolerance Respiratory Status: Airway Patency Activity Tolerance Activity Tolerance 10 Gastrointestinal Function Respiratory Status: Gas Nutritional Status Fluid Balance Exchange 11 Anxiety Level Activity Tolerance Knowledge: Illness Care Anxiety Level 12 Nutritional Status Knowledge: Illness Care Anxiety Level Nutritional Status 13 Respiratory Status: Airway Tissue Integrity: Skin and Gastrointestinal Function Pain Control Patency Mucous Membranes 14 Urinary Elimination Nutritional Status Endurance Self-Care: Activities of Daily Living(ADL)

158 141 Table 15 Continued 15 Respiratory Status: Gas Tissue Perfusion: Pulmonary Respiratory Status: Gas Bowel Elimination Exchange Exchange 16 Knowledge: Illness Care Endurance Respiratory Status: Airway Blood Loss Severity Patency 17 Blood Loss Severity Grief Resolution Fluid Balance Knowledge: Treatment Regimen 18 Aspiration Prevention Oral Hygiene Tissue Perfusion: Pulmonary Knowledge: Illness Care 19 Tissue Perfusion: Pulmonary Fluid Balance Bowel Elimination Coping 20 Respiratory Status: Ventilation Coping Knowledge: Treatment Regimen Tissue Integrity: Skin and Mucous Membranes 21 Endurance Family Coping Pain: Disruptive Effects Respiratory Status: Gas Exchange 22 Coping Aspiration Prevention Blood Loss Severity Respiratory Status: Airway Patency 23 Fluid Balance Acute Confusion Level Urinary Elimination Tissue Perfusion: Pulmonary 24 Knowledge: Treatment Regimen Respiratory Status: Ventilation Coping Respiratory Status: Ventilation 25 Bowel Elimination Spiritual Health Respiratory Status: Ventilation Body Image 26 Swallowing Status Fluid Overload Severity Acute Confusion Level Aspiration Prevention 27 Pain: Disruptive Effects Self-Care: Activities of Daily Fluid Overload Severity Acute Confusion Level Living(ADL) 28 Tissue Perfusion: Cerebral Dignified Life Closure Aspiration Prevention Fluid Overload Severity 29 Oral Hygiene Pain: Disruptive Effects Grief Resolution Tissue Perfusion: Cerebral 30 Acute Confusion Level Tissue Perfusion: Cerebral Self-Care: Activities of Daily Living(ADL) Neurological Status

159 142 Table 15 Continued 31 Tissue Perfusion: Peripheral Cognitive Orientation Tissue Perfusion: Peripheral Tissue Perfusion: Cardiac 32 Grief Resolution Bowel Elimination Oral Hygiene Seizure Control 33 Self-Care: Activities of Daily Neurological Status Tissue Perfusion: Cardiac Gastrointestinal Function Living(ADL) 34 Cognitive Orientation Cardiac Pump Effectiveness Neurological Status Urinary Elimination 35 Neurological Status Swallowing Status Cardiac Pump Effectiveness Grief Resolution 36 Tissue Perfusion: Cardiac Tissue Perfusion: Cardiac Family Coping Family Coping 37 Neurological Status: Seizure Control Spiritual Health Pain: Disruptive Effects Peripheral 38 Body Image Gastrointestinal Function Swallowing Status Spiritual Health 39 Family Coping Cognition Dignified Life Closure Cognitive Orientation 40 Seizure Control Blood Loss Severity Tissue Perfusion: Cerebral Swallowing Status 41 Cognition Knowledge: Treatment Cognitive Orientation Dignified Life Closure Regimen 42 Fluid Overload Severity Tissue Perfusion: Peripheral Seizure Control Tissue Perfusion: Peripheral 43 Spiritual Health Neurological Status: Peripheral Cognition Cardiac Pump Effectiveness 44 Dignified Life Closure Heedfulness of Affected Side Neurological Status: Peripheral Cognition 45 Cardiac Pump Effectiveness Body Image Body Image Neurological Status: Peripheral 46 Heedfulness of Affected Side Urinary Elimination Heedfulness of Affected Side Heedfulness of Affected Side Note. #= Rank

160 143 Table 16 Ranking of Nursing Diagnoses (NANDA-I) by Length of Stay (LOS) Group 1 LOS<1 Group 2 1 LOS<3 Group 3 LOS 3 NANDA-I n % n % n % p Acute Pain Risk for Infection* <.0001 Nausea* Impaired Skin Integrity Risk for Falls* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery Activity Intolerance* <.0001 Deficient Knowledge, Disease Process* <.0001 Anxiety* Imbalanced Nutrition: Less than Body Requirements* <.0001 Risk for Constipation* <.0001 Ineffective Airway Clearance* <.0001 Impaired Gas Exchange* <.0001 Risk for Impaired Skin Integrity* Impaired Physical Mobility Fatigue Ineffective Tissue Perfusion: Pulmonary* <.0001 Chronic Pain* Risk for Imbalanced Fluid Volume* <.0001

161 144 Table 16 Continued Urinary Retention Impaired Oral Mucous Membrane Grieving Risk for Deficient Fluid Volume Ineffective Breathing Pattern Risk for Bleeding Deficient Knowledge Risk for Aspiration Risk for Imbalanced Body Temperature Acute Confusion Sleep Deprivation Deficient Fluid Volume Diarrhea* <.0001 Excess Fluid Volume Readiness for Enhanced Family Coping Ineffective Tissue Perfusion, Cerebral Self-Care Deficit Impaired Swallowing Decreased Intracranial Adaptive Capacity Ineffective Tissue Perfusion: Cardiac Spiritual Distress Bathing/Hygiene Self-Care Deficit Constipation

162 145 Table 16 Continued Decreased Cardiac Output Disturbed Thought Processes Impaired Verbal Communication Impaired Spontaneous Ventilation Risk for Activity Intolerance Impaired Bed Mobility Feeding: Self-Care Deficit Ineffective Tissue Perfusion, Renal Risk for Unstable Blood Glucose Disturbed Body Image Risk for Injury Risk for Peripheral Neurovascular Dysfunction Risk for Withdrawal: Alcohol/Drugs Deficient Knowledge, Insulin Therapy Disabled Family Coping Ineffective Tissue Perfusion Noncompliance Readiness for Enhanced Spiritual Well-Being Impaired Memory Ineffective Coping Unilateral Neglect Impaired Urinary Elimination Ineffective Tissue Perfusion, Peripheral

163 146 Table 16 Continued Social Isolation Disturbed Sensory Perception, Visual Risk for Latex Allergy Response Bowel Incontinence Chronic Confusion Ineffective Health Maintenance Disturbed Sensory Perception, Kinesthetic Disturbed Sensory Perception, Auditory Dysfunctional Ventilatory Weaning Response Impaired Social Interaction Impaired Tissue Integrity Mood Alteration: Depression Risk for Suicide Systemic Risk for Latex Allergy Response Autonomic Dysreflexia Delayed Growth and Development Inadequate Oral Food Beverage Intake Interrupted Family Process Mood Alteration: Mania Readiness for Enhanced Coping Risk for Autonomic Dysreflexia Risk for Self-Directed Violence Situational Low Self-Esteem Toileting Self-Care Deficit

164 147 Note. LOS=Length of stay (day) A total of 2,237 samples, Group 1 = LOS < 1 (n=61, 3%), Group 2 = 1 LOS <3 (n=1,161, 52%), Group 3 = LOS 3 (n=1,015, 45%) n in parenthesis = number of patients in each group of LOS % in parenthesis = percentage of patients in each group of LOS *=significant finding Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=88) were applied, and p value = was adjusted from α =.05.

165 148 Table 17 Ranking of Nursing Diagnoses (NANDA-I) Found Significantly Different by LOS # Group 1: LOS<1 Group 2: 1 LOS < 3 Group 3: LOS 3 1 Ineffective Airway Clearance Risk for Infection Risk for Infection 2 Impaired Gas Exchange Nausea Nausea 3 Risk for Infection Risk for Falls Risk for Falls 4 Ineffective Tissue Perfusion: Pulmonary Activity Intolerance Activity Intolerance 5 Risk for Falls Deficient Knowledge, Disease Process Deficient Knowledge, Disease Process 6 Activity Intolerance Anxiety Imbalanced Nutrition: Less than Body Requirements 7 Deficient Knowledge, Disease Process Imbalanced Nutrition: Less than Body Anxiety Requirements 8 Nausea Risk for Constipation Risk for Constipation 9 Anxiety Ineffective Airway Clearance Ineffective Airway Clearance 10 Imbalanced Nutrition: Less than Body Impaired Gas Exchange Risk for Impaired Skin Integrity Requirements 11 Risk for Imbalanced Fluid Volume Risk for Impaired Skin Integrity Impaired Gas Exchange 12 Risk for Impaired Skin Integrity Ineffective Tissue Perfusion: Pulmonary Chronic Pain 13 Diarrhea Chronic Pain Risk for Imbalanced Fluid Volume 14 Chronic Pain Risk for Imbalanced Fluid Volume Ineffective Tissue Perfusion: Pulmonary 15 Risk for Constipation Diarrhea Diarrhea 16 Ineffective Airway Clearance Risk for Infection Risk for Infection 17 Impaired Gas Exchange Nausea Nausea 18 Risk for Infection Risk for Falls Risk for Falls

166 149 Table 18 Ranking of Nursing Interventions (NIC) by Length of Stay (LOS) Group 1 LOS<1 Group 2 1 LOS<3 Group 3 LOS 3 NIC n % n % n % p Pain Management Fall Prevention* <.0001 Infection Protection* <.0001 Infection Control* <.0001 Nausea Management Teaching: Procedure/Treatment Analgesic Administration Skin Surveillance Wound Care Pressure Management Energy Management* <.0001 Teaching: Preoperative Activity Therapy* <.0001 Exercise Promotion: Strength Training* <.0001 Anxiety Reduction* Nutrition Management* <.0001 Nutrition Therapy* <.0001 Ventilation Assistance* <.0001 Teaching: Disease Process* <.0001 Bowel Management* <.0001

167 150 Table 18 Continued Diet Staging* <.0001 Airway Management* <.0001 Airway Suctioning* <.0001 Acid-Base Management: Respiratory Acidosis* <.0001 Acid-Base Management* <.0001 Fluid Management Exercise Promotion Fluid Monitoring* <.0001 Aspiration Precautions Urinary Retention Care Temperature Regulation Coping Enhancement Oral Health Restoration Grief Work Facilitation Self-Care Assistance Bleeding Precaution Neurologic Monitoring Nutritional Monitoring Teaching: Individual Diarrhea Management* <.0001 Delirium Management Health Education Cardiac Care: Acute

168 151 Table 18 Continued Emotional Support Circulatory Care: Venous Insufficiency Circulatory Care: Arterial Insufficiency Sleep Enhancement Cognitive Stimulation Cognitive Restructuring Family Support Pressure Ulcer Care Cerebral Perfusion Promotion Cerebral Edema Management Dying Care Positioning Constipation/Impaction Management Seizure Precaution Spiritual Support Communication Enhancement: Speech Deficit Artificial Airway Management Environmental Management Active Listening Substance Use Treatment: Alcohol Withdrawal Hyperglycemia Management Hypoglycemia Management Peripheral Sensation Management

169 152 Table 18 Continued Spiritual Growth Facilitation Body Image Enhancement Mutual Goal Setting Socialization Enhancement Family Therapy Memory Training Reality Orientation Self-Responsibility Facilitation Unilateral Neglect Management Substance Use Treatment: Drug Withdrawal Latex Precautions Urinary Habit Training Behavior Management: Self-Harm Self-Care Assistance: Feeding Communication Enhancement: Hearing Deficit Communication Enhancement: Visual Deficit Foot Care Hallucination Management Seizure Management Exercise Therapy: Balance Dysreflexia Management Impulse Control Training Mechanical Ventilatory Weaning

170 153 Table 18 Continued Self-Care Assistance: Toileting Self-Esteem Enhancement Suicide Prevention Surveillance: Safety Bowel Incontinence Care Family Process Maintenance Health Screening Hyperglycemia Management Mood Management Nutrition Education Parenting Promotion Self-Care Assistance: Bathing/Hygiene Self-Care Assistance: Dressing/grooming Teaching: preoperative Note. LOS=Length of stay (day), *=significant finding, A total of 2,237 samples, Group One = LOS < 1 (n=61, 3%), Group Two = 1 LOS <3 (n=1,161, 52%), Group Three = LOS 3 (n=1,015, 45%) Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=100) were applied, and p value =.0005 was adjusted from α =.05.

171 154 Table 19 Ranking of NIC Outcomes Found Significantly Different by LOS # Group 1: LOS<1 Group 2: 1 LOS < 3 Group 3: LOS 3 1 Airway Management Airway Management Infection Protection 2 Ventilation Assistance Ventilation Assistance Infection Control 3 Airway Suctioning Airway Suctioning Analgesic Administration 4 Acid-Base Management: Respiratory Acid-Base Management: Respiratory Fall Prevention Acidosis Acidosis 5 Acid-Base Management Acid-Base Management Energy Management 6 Infection Protection Infection Protection Activity Therapy 7 Infection Control Infection Control Exercise Promotion: Strength Training 8 Analgesic Administration Analgesic Administration Anxiety Reduction 9 Fall Prevention Fall Prevention Nutrition Therapy 10 Energy Management Energy Management Nutrition Management 11 Activity Therapy Activity Therapy Teaching: Disease Process 12 Exercise Promotion: Strength Training Exercise Promotion: Strength Training Bowel Management 13 Teaching: Disease Process Teaching: Disease Process Diet Staging 14 Anxiety Reduction Anxiety Reduction Airway Management 15 Nutrition Management Nutrition Management Airway Suctioning 16 Nutrition Therapy Nutrition Therapy Ventilation Assistance 17 Fluid Monitoring Fluid Monitoring Acid-Base Management: Respiratory Acidosis 18 Diarrhea Management Diarrhea Management Fluid Monitoring 19 Bowel Management Bowel Management Acid-Base Management 20 Diet Staging Diet Staging Diarrhea Management Note. LOS=Length of stay (day), *=significant finding; A total of 2,237 samples, Group One = LOS < 1 (n=61, 3%), Group Two = 1 LOS <3 (n=1,161, 52%), Group Three = LOS 3 (n=1,015, 45%), Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=100) were applied, and p value =.0005 was adjusted from α =.05.

172 155 Table 20 Ranking of Nursing Outcomes (NOC) by Length of Stay (LOS) Group 1: LOS<1 Group 2: 1 LOS<3 Group 3: LOS 3 NOC n % n % n % p Pain Level Infection Severity* <.0001 Nausea and Vomiting Severity* Knowledge: Treatment Procedure* Tissue Integrity: Skin and Mucous Membranes Pain Control Knowledge: Fall Prevention* <.0001 Fall Prevention: Behavior* <.0001 Activity Tolerance* <.0001 Anxiety Level* Nutritional Status* <.0001 Knowledge: Illness Care* <.0001 Gastrointestinal Function* <.0001 Respiratory Status: Airway Patency* <.0001 Respiratory Status: Gas Exchange* <.0001 Mobility Endurance Tissue Perfusion: Pulmonary* <.0001 Hydration* <.0001 Fluid Balance

173 156 Table 20 Continued Urinary Elimination Aspiration Prevention Coping Oral Hygiene Grief Resolution Self-Care: Activities of Daily Living(ADL) Bowel Elimination* <.0001 Blood Loss Severity Respiratory Status: Ventilation Knowledge: Treatment Regimen Family Coping Acute Confusion Level Pain: Disruptive Effects Risk Control: Hyperthermia Risk Control: Hypothermia Sleep Fluid Overload Severity Spiritual Health Tissue Perfusion: Cerebral Cognitive Orientation Neurological Status Swallowing Status Dignified Life Closure

174 157 Table 20 Continued Tissue Perfusion: Cardiac Tissue Perfusion: Peripheral Cardiac Pump Effectiveness Communication Seizure Control Cognition Blood Glucose Level Body Positioning: Self-Initiated Kidney Function Burn Healing Neurological Status: Peripheral Substance Withdrawal Severity Body Image Compliance Behavior Diabetes Self-Management Knowledge: Personal Safety Heedfulness of Affected Side Memory Allergic Response: Systemic Sensory Function: Vision Urinary Continence Nutritional Status: Food and Fluid Intake Social Involvement

175 158 Table 20 Continued Health Seeking Behavior Social Interaction Skills Balance Communication: Receptive Depression Level Neurological Status: Autonomic Risk Control Self-Care Status Suicide Self-Restraint Family Integrity Growth Mood Equilibrium Oral Intake Self-Esteem Self-Mutilation Restraint Note. LOS=Length of stay (day), *=significant finding, a total of 2,237 samples, Group One = LOS < 1 (n=61, 3%), Group Two = 1 LOS <3 (n=1,161, 52%), Group Three = LOS 3 (n=1,015, 45%) Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=81) were applied, and p value = was adjusted from α =.05.

176 159 Table 21 Ranking of NOC Outcomes Found Significantly Different by LOS # Group 1: LOS<1 Group 2: 1 LOS < 3 Group 3: LOS 3 1 Knowledge: Treatment Procedure Infection Severity Infection Severity 2 Respiratory Status: Airway Patency Nausea and Vomiting Severity Nausea and Vomiting Severity 3 Respiratory Status: Gas Exchange Knowledge: Fall Prevention Knowledge: Treatment Procedure 4 Infection Severity Fall Prevention: Behavior Knowledge: Fall Prevention 5 Tissue Perfusion: Pulmonary Activity Tolerance Fall Prevention: Behavior 6 Knowledge: Fall Prevention Anxiety Level Activity Tolerance 7 Fall Prevention: Behavior Nutritional Status Nutritional Status 8 Activity Tolerance Knowledge: Illness Care Anxiety Level 9 Knowledge: Illness Care Gastrointestinal Function Gastrointestinal Function 10 Nausea and Vomiting Severity Respiratory Status: Airway Patency Knowledge: Illness Care 11 Anxiety Level Respiratory Status: Gas Exchange Respiratory Status: Airway Patency 12 Nutritional Status Knowledge: Treatment Procedure Respiratory Status: Gas Exchange 13 Hydration Tissue Perfusion: Pulmonary Hydration 14 Bowel Elimination Hydration Tissue Perfusion: Pulmonary 15 Gastrointestinal Function Bowel Elimination Bowel Elimination Note. LOS=Length of stay (day) A total of 2,237 samples, Group 1 = LOS < 1 (n=61, 3%), Group 2 = 1 LOS <3 (n=1,161, 52%), Group 3 = LOS 3 (n=1,015, 45%), #=rank

177 160 Table 22 The Top Ten linkages of Nursing Diagnoses (NANDA-I), Nursing Interventions (NIC), and Nursing-Sensitive Patient Outcomes (NOC) # NANDA-I NIC NOC n 1 Acute Pain Pain Management Pain Level 1,735 2 Risk for Infection Infection Protection Infection Severity Risk for Infection Infection Control Infection Severity Nausea Nausea Management Nausea and Vomiting Severity Acute Pain Pain Management Pain Control Impaired Skin Integrity Skin Surveillance Tissue Integrity: Skin and Mucous Membranes Impaired Skin Integrity Wound Care Tissue Integrity: Skin and Mucous Membranes 8 Risk for Falls Fall Prevention Knowledge: Fall Prevention Risk for Falls Fall Prevention Fall Prevention: Behavior Deficient Knowledge Teaching: Procedure/ Knowledge: Treatment 347 Pre/Post Treatment Procedure Procedure/Surgery 390

178 161 Table 23 Patterns of Nursing Diagnoses (NANDA-I) Combinations Nursing Diagnoses n % Row % Col % p Acute Pain & Risk for Infection Acute Pain & Nausea* Acute Pain & Impaired Skin Integrity* <.0001 Acute Pain & Risk for Falls Acute Pain & Deficient Knowledge Pre/Post Procedure/Surgery* <.0001 Acute Pain & Activity Intolerance Acute Pain & Anxiety Acute Pain & Imbalanced Nutrition: Less than Body Requirements* <.0001 Acute Pain & Risk for Constipation* <.0001 Acute Pain & Ineffective Airway Clearance Acute Pain & Fatigue* <.0001 Acute Pain & Risk for Imbalanced Fluid Volume Acute Pain & Urinary Retention* <.0001 Acute Pain & Grieving Risk for Infection & Nausea Risk for Infection & Impaired Skin Integrity* <.0001 Risk for Infection & Risk for Falls* <.0001 Risk for Infection & Deficient Knowledge Pre/Post Procedure/Surgery* <.0001 Risk for Infection & Activity Intolerance* <.0001 Risk for Infection & Anxiety* <.0001 Risk for Infection & Imbalanced Nutrition: Less than Body Requirements*

179 162 Table 23 Continued Risk for Infection & Risk for Constipation* <.0001 Risk for Infection & Ineffective Airway Clearance Risk for Infection & Fatigue* <.0001 Risk for Infection & Risk for Imbalanced Fluid Volume* <.0001 Risk for Infection & Urinary Retention Risk for Infection & Grieving Nausea & Impaired Skin Integrity Nausea & Risk for Falls Nausea & Deficient Knowledge Pre/Post Procedure/Surgery Nausea & Activity Intolerance* <.0001 Nausea & Anxiety* <.0001 Nausea & Imbalanced Nutrition: Less than Body Requirements* Nausea & Risk for Constipation* <.0001 Nausea & Ineffective Airway Clearance* Nausea & Fatigue* <.0001 Nausea & Risk for Imbalanced Fluid Volume Nausea & Urinary Retention* <.0001 Impaired Skin Integrity & Grieving Impaired Skin Integrity & Risk for Falls* <.0001 Impaired Skin Integrity & Deficient Knowledge Pre/Post Procedure/Surgery* <.0001 Impaired Skin Integrity & Activity Intolerance* <.0001 Impaired Skin Integrity & Anxiety*

180 163 Table 23 Continued Impaired Skin Integrity & Imbalanced Nutrition: Less than Body Requirements Impaired Skin Integrity & Risk for Constipation* <.0001 Impaired Skin Integrity & Ineffective Airway Clearance Impaired Skin Integrity & Fatigue* <.0001 Impaired Skin Integrity & Risk for Imbalanced Fluid Volume* Impaired Skin Integrity & Urinary Retention Impaired Skin Integrity & Grieving Risk for Falls & Deficient Knowledge Pre/Post Procedure/Surgery* <.0001 Risk for Falls & Activity Intolerance* <.0001 Risk for Falls & Anxiety* <.0001 Risk for Falls & Imbalanced Nutrition: Less than Body Requirements* <.0001 Risk for Falls & Risk for Constipation* Risk for Falls & Ineffective Airway Clearance Risk for Falls & Fatigue Risk for Falls & Risk for Imbalanced Fluid Volume* <.0001 Risk for Falls & Urinary Retention Risk for Falls & Grieving* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery & Activity Intolerance* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery & Anxiety* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery & Imbalanced Nutrition: Less than Body Requirements Deficient Knowledge Pre/Post Procedure/Surgery & Risk for Constipation* <.0001

181 164 Table 23 Continued Deficient Knowledge Pre/Post Procedure/Surgery & Ineffective Airway Clearance Deficient Knowledge Pre/Post Procedure/Surgery & Fatigue Deficient Knowledge Pre/Post Procedure/Surgery & Risk for Imbalanced Fluid Volume* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery & Urinary Retention* <.0001 Deficient Knowledge Pre/Post Procedure/Surgery & Grieving Activity Intolerance & Anxiety* <.0001 Activity Intolerance & Imbalanced Nutrition: Less than Body Requirements* <.0001 Activity Intolerance & Risk for Constipation* <.0001 Activity Intolerance & Ineffective Airway Clearance Activity Intolerance & Fatigue* <.0001 Activity Intolerance & Risk for Imbalanced Fluid Volume* <.0001 Activity Intolerance & Urinary Retention* <.0001 Activity Intolerance & Grieving Anxiety & Imbalanced Nutrition: Less than Body Requirements* <.0001 Anxiety & Risk for Constipation Anxiety & Ineffective Airway Clearance* Anxiety & Fatigue* <.0001 Anxiety & Risk for Imbalanced Fluid Volume* <.0001 Anxiety & Urinary Retention* Anxiety & Grieving Imbalanced Nutrition: Less than Body Requirements & Risk for Constipation*

182 165 Table 23 Continued Imbalanced Nutrition: Less than Body Requirements & Ineffective Airway Clearance Imbalanced Nutrition: Less than Body Requirements & Fatigue* <.0001 Imbalanced Nutrition: Less than Body Requirements & Risk for Imbalanced Fluid Volume* <.0001 Imbalanced Nutrition: Less than Body Requirements & Urinary Retention* Imbalanced Nutrition: Less than Body Requirements & Grieving Risk for Constipation & Ineffective Airway Clearance Risk for Constipation & Fatigue Risk for Constipation & Risk for Imbalanced Fluid Volume* <.0001 Risk for Constipation & Urinary Retention* <.0001 Risk for Constipation & Grieving Ineffective Airway Clearance & Fatigue Ineffective Airway Clearance & Risk for Imbalanced Fluid Volume Ineffective Airway Clearance & Urinary Retention Ineffective Airway Clearance & Grieving* <.0001 Fatigue & Risk for Imbalanced Fluid Volume Fatigue & Urinary Retention Fatigue & Grieving Risk for Imbalanced Fluid Volume & Urinary Retention* <.0001 Risk for Imbalanced Fluid Volume & Grieving Urinary Retention & Grieving Note. *= significant findings; Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=100) was applied, and p value =.0005 was adjusted from α =.05.; Row %=Row percentage; Col %=Column percentage

183 166 Table 24 Pattern of Nursing Interventions (NIC) Combinations NIC (nursing interventions) n % Row% Col% p Pain Management & Fall Prevention Pain Management & Infection Protection* Pain Management & Nausea Management* Pain Management & Teaching: Procedure/Treatment Pain Management & Skin Surveillance* <.0001 Pain Management & Energy Management* Pain Management & Anxiety Reduction Pain Management & Nutrition Management* Pain Management & Ventilation Assistance Pain Management & Bowel Management* <.0001 Pain Management & Fluid Management* <.0001 Pain Management & Urinary Retention Care* <.0001 Pain Management & Temperature Regulation Pain Management & Oral Health Restoration* <.0001 Fall Prevention & Infection Protection* <.0001 Fall Prevention & Nausea Management Fall Prevention & Teaching: Procedure/Treatment* <.0001 Fall Prevention & Skin Surveillance* <.0001 Fall Prevention & Energy Management* <.0001 Fall Prevention & Anxiety Reduction* <.0001 Fall Prevention & Nutrition Management* <.0001 Fall Prevention & Ventilation Assistance Fall Prevention & Bowel Management

184 167 Table 24 Continued Fall Prevention & Fluid Management Fall Prevention & Urinary Retention Care Fall Prevention & Temperature Regulation* Fall Prevention & Oral Health Restoration* Infection Protection & Nausea Management Infection Protection & Teaching: Procedure/Treatment* <.0001 Infection Protection & Skin Surveillance* <.0001 Infection Protection & Energy Management* <.0001 Infection Protection & Anxiety Reduction* <.0001 Infection Protection & Nutrition Management* Infection Protection & Ventilation Assistance Infection Protection & Bowel Management* <.0001 Infection Protection & Fluid Management Infection Protection & Urinary Retention Care* Infection Protection & Temperature Regulation Infection Protection & Oral Health Restoration Nausea Management & Teaching: Procedure/Treatment Nausea Management & Skin Surveillance Nausea Management & Energy Management* <.0001 Nausea Management & Anxiety Reduction* <.0001 Nausea Management & Nutrition Management Nausea Management & Ventilation Assistance* <.0001 Nausea Management & Bowel Management* <.0001 Nausea Management & Fluid Management Nausea Management & Urinary Retention Care* Nausea Management & Temperature Regulation

185 168 Table 24 Continued Nausea Management & Oral Health Restoration Teaching: Procedure/Treatment & Skin Surveillance* <.0001 Teaching: Procedure/Treatment & Energy Management* <.0001 Teaching: Procedure/Treatment & Anxiety Reduction* <.0001 Teaching: Procedure/Treatment & Nutrition Management* <.0001 Teaching: Procedure/Treatment & Ventilation Assistance Teaching: Procedure/Treatment & Bowel Management Teaching: Procedure/Treatment & Fluid Management Teaching: Procedure/Treatment & Urinary Retention Care Teaching: Procedure/Treatment & Temperature Regulation* <.0001 Teaching: Procedure/Treatment & Oral Health Restoration Skin Surveillance & Energy Management Skin Surveillance & Anxiety Reduction* Skin Surveillance & Nutrition Management Skin Surveillance & Ventilation Assistance Skin Surveillance & Bowel Management* <.0001 Skin Surveillance & Fluid Management* Skin Surveillance & Urinary Retention Care Skin Surveillance & Temperature Regulation* <.0001 Skin Surveillance & Oral Health Restoration Energy Management & Anxiety Reduction* <.0001 Energy Management & Nutrition Management* <.0001 Energy Management & Ventilation Assistance* Energy Management & Bowel Management* <.0001 Energy Management & Fluid Management* <.0001 Energy Management & Urinary Retention Care* <.0001

186 169 Table 24 Continued Energy Management & Temperature Regulation* Energy Management & Oral Health Restoration Anxiety Reduction & Nutrition Management* <.0001 Anxiety Reduction & Ventilation Assistance* Anxiety Reduction & Bowel Management Anxiety Reduction & Fluid Management Anxiety Reduction & Urinary Retention Care Anxiety Reduction & Temperature Regulation* Anxiety Reduction & Oral Health Restoration* <.0001 Nutrition Management & Ventilation Assistance Nutrition Management & Bowel Management* Nutrition Management & Fluid Management* <.0001 Nutrition Management & Urinary Retention Care Nutrition Management & Temperature Regulation* <.0001 Nutrition Management & Oral Health Restoration Ventilation Assistance & Bowel Management Ventilation Assistance & Fluid Management Ventilation Assistance & Urinary Retention Care Ventilation Assistance & Temperature Regulation Ventilation Assistance & Oral Health Restoration Bowel Management & Fluid Management Bowel Management & Urinary Retention Care* <.0001 Bowel Management & Temperature Regulation* <.0001 Bowel Management & Oral Health Restoration Fluid Management & Urinary Retention Care Fluid Management & Temperature Regulation

187 170 Table 24 Continued Fluid Management & Oral Health Restoration Urinary Retention Care & Temperature Regulation* <.0001 Urinary Retention Care & Oral Health Restoration Temperature Regulation & Oral Health Restoration Note. *=significant findings at α =.05, p value =.0005 was adjusted from p=.05 after Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=100) Row %=Row percentage, Col %=Column percentage

188 171 Table 25 Pattern of Nursing-Sensitive Patient Outcomes (NOC) Combinations Nursing-sensitive patient outcomes n % Row% Col% p Pain Level & Infection Severity Pain Level & Nausea and Vomiting Severity* <.0001 Pain Level & Knowledge: Treatment Procedure Pain Level & Tissue Integrity: Skin and Mucous Membranes* <.0001 Pain Level & Knowledge: Fall Prevention Pain Level & Activity Tolerance Pain Level & Anxiety Level Pain Level & Nutritional Status* Pain Level & Respiratory Status: Airway Patency Pain Level & Hydration Pain Level & Urinary Elimination* Pain Level & Coping* <.0001 Pain Level & Oral Hygiene Pain Level & Grief Resolution Infection Severity & Nausea and Vomiting Severity Infection Severity & Knowledge: Treatment Procedure* <.0001 Infection Severity & Tissue Integrity: Skin and Mucous Membranes* <.0001 Infection Severity & Knowledge: Fall Prevention* <.0001 Infection Severity & Activity Tolerance* <.0001 Infection Severity & Anxiety Level* <.0001 Infection Severity & Nutritional Status*

189 172 Table 25 Continued Infection Severity & Respiratory Status: Airway Patency Infection Severity & Hydration* <.0001 Infection Severity & Urinary Elimination Infection Severity & Coping Infection Severity & Oral Hygiene* <.0001 Infection Severity & Grief Resolution Nausea and Vomiting Severity & Knowledge: Treatment Procedure Nausea and Vomiting Severity & Tissue Integrity: Skin and Mucous Membranes Nausea and Vomiting Severity & Knowledge: Fall Prevention Nausea and Vomiting Severity & Activity Tolerance* <.0001 Nausea and Vomiting Severity & Anxiety Level* <.0001 Nausea and Vomiting Severity & Nutritional Status* Nausea and Vomiting Severity & Respiratory Status: Airway Patency* Nausea and Vomiting Severity & Hydration Nausea and Vomiting Severity & Urinary Elimination* <.0001 Nausea and Vomiting Severity & Coping Nausea and Vomiting Severity & Oral Hygiene* <.0001 Nausea and Vomiting Severity & Grief Resolution Knowledge: Treatment Procedure & Tissue Integrity: Skin and Mucous Membranes* <.0001 Knowledge: Treatment Procedure & Knowledge: Fall Prevention* <.0001 Knowledge: Treatment Procedure & Activity Tolerance* <.0001 Knowledge: Treatment Procedure & Anxiety Level* <.0001 Knowledge: Treatment Procedure & Nutritional Status* <.0001 Knowledge: Treatment Procedure & Respiratory Status: Airway Patency

190 173 Table 25 Continued Knowledge: Treatment Procedure & Hydration* <.0001 Knowledge: Treatment Procedure & Urinary Elimination Knowledge: Treatment Procedure & Coping Knowledge: Treatment Procedure & Oral Hygiene* <.0001 Knowledge: Treatment Procedure & Grief Resolution Tissue Integrity: Skin and Mucous Membranes & Knowledge: Fall Prevention* <.0001 Tissue Integrity: Skin and Mucous Membranes & Activity Tolerance* <.0001 Tissue Integrity: Skin and Mucous Membranes & Anxiety Level* <.0001 Tissue Integrity: Skin and Mucous Membranes & Nutritional Status Tissue Integrity: Skin and Mucous Membranes & Respiratory Status: Airway Patency Tissue Integrity: Skin and Mucous Membranes & Hydration* <.0001 Tissue Integrity: Skin and Mucous Membranes & Urinary Elimination Tissue Integrity: Skin and Mucous Membranes & Coping Tissue Integrity: Skin and Mucous Membranes & Oral Hygiene Tissue Integrity: Skin and Mucous Membranes & Grief Resolution Knowledge: Fall Prevention & Activity Tolerance* <.0001 Knowledge: Fall Prevention & Anxiety Level* <.0001 Knowledge: Fall Prevention & Nutritional Status* <.0001 Knowledge: Fall Prevention & Respiratory Status: Airway Patency Knowledge: Fall Prevention & Hydration* <.0001 Knowledge: Fall Prevention & Urinary Elimination Knowledge: Fall Prevention & Coping Knowledge: Fall Prevention & Oral Hygiene Knowledge: Fall Prevention & Grief Resolution* <.0001

191 174 Table 25 Continued Activity Tolerance & Anxiety Level* <.0001 Activity Tolerance & Nutritional Status* <.0001 Activity Tolerance & Respiratory Status: Airway Patency Activity Tolerance & Hydration* <.0001 Activity Tolerance & Urinary Elimination* <.0001 Activity Tolerance & Coping Activity Tolerance & Oral Hygiene* <.0001 Activity Tolerance & Grief Resolution Anxiety Level & Nutritional Status* <.0001 Anxiety Level & Respiratory Status: Airway Patency* Anxiety Level & Hydration* <.0001 Anxiety Level & Urinary Elimination Anxiety Level & Coping* <.0001 Anxiety Level & Oral Hygiene* <.0001 Anxiety Level & Grief Resolution Nutritional Status & Respiratory Status: Airway Patency Nutritional Status & Hydration* <.0001 Nutritional Status & Urinary Elimination* Nutritional Status & Coping Nutritional Status & Oral Hygiene* <.0001 Nutritional Status & Grief Resolution Respiratory Status: Airway Patency & Hydration Respiratory Status: Airway Patency & Urinary Elimination Respiratory Status: Airway Patency & Coping

192 175 Table 25 Continued Respiratory Status: Airway Patency & Oral Hygiene Respiratory Status: Airway Patency & Grief Resolution* <.0001 Hydration & Urinary Elimination* <.0001 Hydration & Coping Hydration & Oral Hygiene Hydration & Grief Resolution Urinary Elimination & Coping Urinary Elimination & Oral Hygiene Urinary Elimination & Grief Resolution Coping & Oral Hygiene Coping & Grief Resolution Oral Hygiene & Grief Resolution Note. *=significant findings at α =.05, p value = was adjusted from p=.05 after Bonferroni's method (Bailar & Hoaglin, 2012) for multiple comparisons (n=81).

193 176 Table 26 Distribution of Outcome Change Scores for Pain Level Overall 2.08(22) 9.93(105) 35.26(415) 34.63(366) 11.36(118) 2.93(31) Unit* Unit G 1.35(8) 11.45(68) 43.94(261) 33.00(196) 9.43(56) 0.84(5) Unit H 2.04(3) 8.16(12) 42.86(63) 29.25(43) 12.93(19) 4.76(7) Unit M 3.37(10) 7.41(22) 28.96(86) 41.41(123) 13.13(39) 5.72(17) Unit A 5.26(1) 15.79(3) 26.32(5) 21.05(4) 21.05(4) 10.53(2) Gender Female 1.51(16) 6.15(65) 24.88(263) 22.89(242) 6.72(71) 1.51(16) Male 0.57(6) 3.78(40) 14.38(152) 11.73(124) 4.45(47) 1.42(15) Race White 1.99(21) 9.02(95) 34.76(366) 30.01(316) 9.59(101) 2.75(29) AA 0.09(1) 1.33(14) 2.18(23) 0.85(9) Asian 0.09(1) 0.28(3) 0.28(3) AI 0.09(1) 0.19(2) 0.09(1) 0.09(1) Other 0.66(7) 2.66(28) 2.18(23) 0.66(7) 0.09(1) Age 18<Age< (76) 26.80(283) 26.52(280) 8.24(87) 2.37(25) Age (9) 2.75(29) 12.50(132) 8.14(86) 2.84(30) 0.57(6) Treatment SP 2.90(9) 11.61(36) 12.26(38) 1.61(5) 0.97(3) CT 0.65(2) 3.23(10) 2.26(7) 0.65(2) 0.32(1)

194 177 Table 26 Continued RT 0.32(1) 0.32(1) 1.29(4) 0.32(1) SP/RT 2.42(3) 0.81(1) CT/RT 0.97(3) 3.23(10) 2.58(8) 0.97(3) 0.32(1) SP/CT/RT 0.32(1) 4.52(14) 8.71(27) 11.61(36) 3.23(10) 0.97(3) Others 0.32(1) 0.65(2) 7.10(22) 5.81(18) 2.26(7) 1.29(4) Insurance Medicaid 1.04 (11) 3.41(36) 14.00(148) 11.07(117) 4.07(43) 0.95(10) Medicare 0.09(1) 1.42(15) 3.41 (36) 3.50(37) 1.32(14) 0.66(7) Self-pay 0.09(1) 0.47(5) 0.09(1) 0.19(2) Private 0.85(9) 5.11(54) 21.38(226) 19.96(211) 5.58(59) 1.32(14) Note. % (n)=percentage(frequency) *=significant <.001 AA= African American; AI= American Indian; SP=Surgery Procedure; CT=Chemotherapy; RT=Radiotherapy; Self-pay=uninsured Private= Private Insurance

195 178 Table 27 Distribution of Outcome Change Scores for Infection Severity Overall 5.38(19) 15.01(53) 37.68(133) 30.03(106) 7.93(28) 3.97(14) Unit Unit G 3.23(4) 19.35(24) 46.77(58) 23.39(29) 4.84(6) 2.42(5) Unit H 3.75(3) 15(12) 41.25(33) 30(24) 7.5(6) 2.5(2) Unit M 4.2(5) 11.76(14) 27.73(33) 37.82(45) 12.61(15) 5.88(7) Unit A 23.33(7) 10(3) 30(9) 26.67(8) 3.33(1) 6.67(2) Note. n=353

196 179 CHAPTER V DISCUSSION AND CONCLUSION The purpose of this descriptive study was to identify planned nursing care as defined by selected nursing diagnoses, nursing interventions, nursing-sensitive patient outcomes using standardized nursing terminologies from an EHR for patients in four oncology specialty units in a 762-bed Midwestern tertiary hospital. Overview of Study Findings The study sample was on average 55 years of age, female, Caucasian, married, retired, and had private insurance coverage. Type of Unit, Age, Treatment Associated with LOS The characteristics of the specialty units played a more important role in LOS, rather than characteristics of patients. In this study hospital, surgery was the major reason for admission and thereby a shorter LOS was expected. Although age reports the only patient characteristic related to LOS (p =.011), the difference of LOS between two age groups, which was less than one day (3.6 and 3.8) did not provide meaningful information in practice. The type of unit and treatments were associated with LOS. Patients who received a combination of three treatments, including surgery related to cancer, chemotherapy, and radiotherapy had longer LOSs than patients receiving other therapies, such as hormone therapy or immunotherapy. This difference in LOS found across the four units may potentially be based on the type of cancer treated by the specialty the unit. For example, the Adult Leukemia and Bone Marrow Transplant Unit

197 180 had longer LOSs for patients than the specialties in the Hematology, Gynecology, and Medical-Surgical Units. Top Ranking of Nursing Diagnoses (NANDA-I) Acute Pain Acute Pain (Appendix C) was the top nursing diagnosis on three of the units, while Risk for Infection ranked as the top NANDA-I diagnosis in the Adult Leukemia and Bone Marrow Transplant Unit. The rankings may slightly differ in order among units; however, the top ten nursing diagnoses were similar across the four units. Two modified nursing diagnoses relevant to Deficient Knowledge appeared in the higher ranking diagnoses. These modified nursing diagnoses were used frequently by nurses, which may explain their importance to meet the needs of the cancer population admitted to these units. The reasons for nurses selecting a global term instead of a specific NANDA-I diagnosis were not collected in the study. Future research might investigate how nurses make decisions about selecting a global term or a specific one as part of their decisionmaking process. In addition, the top nursing diagnosis, Acute Pain, was recognized more much more frequently than the diagnosis ranked in second place. For the top ten nursing diagnoses, except Nausea, which is obviously related to cancer treatment, there are three nursing diagnoses, which emphasized quality indicators- Risk for Infection, Impaired Skin Integrity and Risk for Falls. This emphasis on patient safety has been a focus in practice since a report published by IOM focused on quality and safety.

198 181 Top Ranking of Nursing Interventions (NIC) Pain Management Pain Management (Appendix D) was the top nursing intervention provided on these units. The ranking of NIC interventions was similar with those for NANDA-I diagnoses since the NIC were chosen based on NANDA-I diagnoses. Nausea Management may be the only specific nursing intervention directly relevant to cancer. In the top ten nursing interventions, there are six nursing interventions relevant to patient safety. These six nursing interventions are Fall Prevention, Infection Protection, Infection Control, Skin Surveillance, Wound Care, and Pressure Management. These six interventions are closely associated with well-known negative outcomes and have been emphasized as indicators of quality care. They are usually called sensitive data in the hospital and also relevant to reimbursement in the current healthcare arena. The finding of a high percentage of interventions focused on patient safety may be due to the promotion of health policy in this decade. However, for this specialty, an intervention such as Nausea Management is also important to an oncology patient to receive a care focused on symptom management. Besides, interventions for patient safety, or interventions to prevent these negative outcomes, nurses should also focus on interventions that improve positive outcomes, especially in a specialty unit. Top Ranking of Nursing-Sensitive Patient Outcomes (NOC) Pain Level Pain Level (Appendix E) was the top nursing-sensitive patient outcome in this study since this NOC is also linked to the NANDA-I diagnosis, Acute Pain. Chronic Pain

199 182 was another NOC outcome, Pain Level. However, outcome change scores for Pain Level in Question Five only referenced to Acute Pain. These outcomes have a different focus because Pain Level measures the severity of pain while Pain Control measures the patient s ability to personally control the pain. In the top ten outcomes, 26% of patients had been evaluated for their Nausea and Vomiting Severity. Only 9% of cancer patients were evaluated for their Nutrition Status during hospitalization. For these patients there is a high prevalence of risk of malnutrition and neglect of nutritional problems by health care providers is a serious problem. In the top ten outcomes, Anxiety Level is the only outcome chosen to address the social-psychological perspective of the patient. This finding suggests that the social-psychical problems faced by patients may need to be more carefully evaluated in spite of short hospital lengths of stay. Top Linkages of NANDA-I, NOC, and NIC (NNN) Acute Pain Pain Level Pain Management The most frequent linkages were between the top rankings of NANDA-I diagnoses, NOC outcomes, and NIC interventions. Other frequent linkages, such as Risk for Infection, Infection Severity, and Infection Protection were also important to denote clinical decision-making of nurses providing oncology nursing care in this hospital. In the researcher s experience, during manual data retrieval of data, no illogical linkages of NNN were identified in the study sample during the seven-month data collection period. Less frequently used linkages are also important to disclose practice patterns in specialty populations. Uncommon nursing diagnoses, interventions or outcomes should not be ignored by both clinical nurses and researchers. First, these uncommon plans of care can provide individualized patient care, which mayelevate the quality of patient care. Second,

200 183 it can be a potential and important plan of care but not yet identified in clinical settings. Third, it can be an inappropriate plan of care due to inaccurate clinical reasoning and priority setting by the nurse. Finally, the linkages provide an excellent educational resource for clinically naïve nurses and nursing students. Nurses are using standardized nursing terminologies to tailor the care plan to fit the needs of individual patients. It is important that the nursing diagnosis is accurately identified since the outcomes and interventions are built on the problem that is identified using NANDA I diagnoses. Patterns of NANDA-I Diagnoses, NIC Interventions, NOC Outcomes Paired relationships between two NANDA-I diagnoses were examined by using the Chi-square tests with Fisher s exact tests (Der & Everitt, 2006). The top fifteen NANDA-I diagnoses, excluding any second closely related nursing diagnosis or concept from a similar domain, were selected and then 105 multiple comparisons using Bonferroni's method (Bailar & Hoaglin, 2012) were applied. The strategies were replicated using NIC interventions and NOC outcomes for a pattern examination. This method brought insight to the emerging field of symptom clusters experienced by oncology patients admitted to the hospital. NANDA-I diagnoses include signs and symptoms in the defining characteristics, which provide a good tool for studies of symptom management. Rather than a symptom, a study of nursing diagnoses would also appropriately disclose patient problems associated with nursing care. Additionally, NANDA-I, a SNT linked to NOC and NIC, would empower researchers to delineate and explore in a more comprehensive and efficient method the defining characteristics and etiologies of specific symptoms experienced by oncology patients for this type of study.

201 184 Outcome Change Scores: Pain Level Outcome Change Scores Mostly Maintained or Improved The descriptive statistics of outcome change scores of the NOC outcome, Pain Level, among the patients with Acute Pain for all units and by unit were addressed. The percentages of outcome change scores for Pain Level in six categories ( -2, -1, 0, 1, 2, 3) by the characteristics of the patients (age, gender, and race) and other variables (treatment, and type of insurance) showed similar distributions. In general, the majority of patients maintained the same outcome rating score or improved their outcome change scores for Pain Level. However, five histograms (all units and each unit) show different shapes of distribution by unit when presented by using percentages of outcomes change scores in the six categories. Most patients discharged from these oncology units remained the same change score ratings or had +1 improvement in change scores from admission to discharge from the hospital. Outcome Change Scores: Infection Severity Infection Severity reported the most frequently used NOC outcomes in Unit A (Adult Leukemia and Bone Marrow Transplant Unit). The distributions of outcome change scores for all units and by unit are reported. Unit A had a greater range of distribution of outcome change scores than the other units. This result conveys that Unit A has higher percentage of patients that either improved or declined in outcome change scores based on Infection Severity than the other three units. This finding demonstrates that these patients are admitted to this specific specialty are subject to the problem of infection.

202 185 Outcome Ratings Use of Outcome Change Scores Use of outcome ratings itself not only demonstrates the nursing profession s contributions to patient outcomes in a scientific way but also benefits the nursing profession in clinical practice, education, and research. Outcome rating scores provide a standard way to evaluate nursing care. Outcomes ratings from each patient record provide evidence of the nursing care provided for patients during the hospitalization. It also provides a mechanism for nursing staff to evaluate their nursing planned care. The outcome scores recorded before and after a nursing intervention that can be used to create a change score provide nursing insight in clinical decision-making and helps nurses decide if they should modify their plan of care. Outcome change scores help determine if patients problems (diagnoses) have been resolved or diminished in a straight forward way. The relationship between outcomes and interventions helps identify the most appropriate interventions for quality patient care. Therefore, outcome change scores can be a measure for researchers, who plan to conduct cost-effectiveness research, Use of outcome change scores can be considered in different ways due to study purposes. In this study, outcome change score was defined by the change in rating scores from admission to discharge. The finding in the study did not show characteristics of patients (age, gender, race), and others (treatments and types of insurance) to be associated with outcome change scores for Pain Level for the patients with a nursing diagnose of Acute Pain. The outcome change scores for each person were calculated from two ratings at admission and at discharge. The variation between these two outcome ratings were not considered in the study due to the patients short LOSs in this acute

203 186 setting (M=3.7 days). But the study did provide initial research to identify any association between patient characteristics and outcome change scores based on the primary nursing diagnoses. For patients with long LOSs, a series of potential fluctuating outcome scores during the hospitalization should be studied. In future studies, the pattern of NOC ratings along with certain procedures, such as nursing interventions or other treatments, in the hospitalization period should also be studied. A mixed design approach should be encouraged to study patterns of outcome change scores in different LOSs. Qualitative methods may provide an alternative to discover some factors potentially associated with the pattern of outcome scores. The outcome change scores can also be used to study the impact of a certain event, intervention, or behavior, For example, the relationship between music therapy and Anxiety Level before and after an operation. The defined change scores then would be the rating before and after the event, such as the Pain Level could be evaluated before and after a surgical procedure. In addition, another factor that should be considered for outcome change scores are characteristics of the disease itself. Patients diagnosed with cancer experience a deteriorating process in many cases when the cancer is progressing. The outcome rating can be influenced by the stage of cancer or by an individual s other chronic conditions. A range of outcome scores is expected in different populations. Therefore, future research may lead to the development of an index of the range of baseline outcome ratings, expected outcome ratings, and outcome change scores for different populations of patients to provide clinical decision support for nurses. Moreover, in order to build such an index of optimum range of outcome ratings, research on factors influencing the range of outcome ratings is required. The

204 187 metaparadigms (person, environment, health, and nursing) need to be considered to address the potential factors. Some examples could be described as: 1) Functional status (ADL) 2) Characteristics of patients related to outcome measures, such as personality and coping/or Anxiety Level; 3) Disease information, such as stage of tumor. 4) Health care resource provided within limited nursing care hours, including environment (treatment purpose in settings, nursing staffing level) Use of Outcome Ratings at Admission An outcome rating should be completed within 24 hours of admission as a similar policy standard for establishing a nursing diagnosis. A first outcome rating serves as the baseline assessment. It is important to obtain the first outcome rating for nurses to plan appropriate care for their patients. This has to be completed once a patient is admitted to a hospital. The NOC rating feature with the five-likert scale provides nurses with a quick, easy way to monitor changes of patients conditions and evaluate the care planned for patients. Outcome ratings at admission and their association with other variables were not addressed in the study. However, it is important to use the first outcome rating scores and related factors to develop an optimum range of baseline outcome rating for this oncology population.

205 188 Use of Outcome Ratings at Discharge An outcome rating at discharge should be studied along with the baseline ratings. The pattern of all ratings during the hospitalization period based on various conditions should be explored. Use of SNTs is an important component of the role of the nurse. It provides a communication tool for nurses. It demonstrates nurses unique and independent function in quality patient care to other healthcare providers. It is essential to invite all nurses in each specialty, such as oncology, gerontology, pediatrics etc., to assure and update completeness and comprehensiveness of SNLs. This completeness of SNTs is also critical to advance nursing information systems. A current underdeveloped nursing information system is an obstacle for successful implementation of SNTs in EHRs. As patients conditions and advanced treatments become more complicated, nurses are challenged to provide evidenced-based care without the support of well-developed nursing information systems that meet the needs of nurses at the bedside. Nurses need a professional language to communicate and document their planned care in practice. Researchers require a well-structured data warehouse to assure the quality of data for exploring and defining the knowledge of nursing practice that is beneficial for quality patient care. This study provided an initial look of how SNTs within a complete set of nursing planned care (nursing diagnoses, nursing interventions, and nursing sensitive patient outcomes) can be used in oncology inpatients. This knowledge can be beneficial to nurses in current oncology inpatient care and expand future research in this area. An outcome score at discharge could serve as a desired outcome in an ideal situation. The better outcome rating at discharge for a patient is expected and lead to a

206 189 lower rate of readmission for oncology patients. A potential study can be designed to examine the outcome ratings at discharge and their association with readmission rate. Study Limitations Population Representation One Site Sample The study findings may not be generalizable to other hospitals because the sample is from one hospital. The geographic population may contribute to some specific characteristics of the patients, such as one specific race and thereby reducing the representation for the general population. For example, the majority of the sample was Caucasian (90%). Nearly 42% of the patients admitted to study units were under investigation for cancer and the majority of patients were newly diagnosed with cancer (M=3 months). The findings may not be generalized to patients with long durations of cancer or to states with different frequencies of types of cancers. The average LOS was 3.7 days, shorter than published for similar populations. The short LOS may potentially lead to more difficulty in generalizing results in other facilities with documented longer LOSs. Patients received three combinations of treatments had longer LOSs. Patients who were treated for Leukemia or undergoing Bone Marrow Transplant stayed longer in the hospital. The cost and other complications such as Risk for Infection would be a concern in this group with prolonged LOSs. Nursing planned care may be modified in this group. For example, Anxiety Level varied in different stages for this population under chemotherapy (Young et al., 2002).

207 190 Data Data Quality The researcher used a variety of strategies, such as regular, random checks during data collection, SAS software (data filtering) and EXCEL (data validation) in the stage of data proceeding and data completion to reduce the potential for errors in the manual data retrieval of nursing planned care in the EHR. The quality of data in manual retrieval is still a concern. In addition, the design of the nursing documentation system also creates a challenge. For example, the system allowed nurses to place the same nursing diagnosis on the same patient when the previous identical nursing diagnosis was not terminated. Duplicate nursing diagnoses confuse nurses when monitoring the change of ratings with the latest outcome rating. It also more time consuming for the researcher to clean the data and assure data quality. The clinical information system should have processes that prevent these duplications. Another factor that influences the data quality is the accuracy of the NNN linkages. No data information relevant to confirm the NNN linkages matched the clinical conditions of patients. The accuracy of the NNN linkages relies on nurses knowledge of SNTs and their familiarity with the nursing documentation system. Although the study hospital had a long history of use of NNN, the computerized system in the study hospital has been implemented for a year prior to the data collection period for this research. An adoption rate of a technology innovation for nurses has to been considered as a possible influence on the data retrieved for research in the initial phases of implementation. The first year data collection may be too short for nurses familiarity both in nursing diagnosis and the use of the computer system based on the adult s learning curve. Although the

208 191 start date was one year after the switch to the new nursing information system for documentation, a naïve nurse without previous training of SNTs in school might experience a longer learning curve to the new knowledge and technology. Therefore, the completeness of computerized nursing documentation has to be considered for data quality, but it was beyond the scope of the study. Nevertheless, overall the data collection process, percentage of complete sets of linkages of NNN can be evaluated as data quality and provides a general idea of nurses competency in clinical decisionmaking based on the logical and rational links between NNN. An irrelevant linkage of NNN is always easily detected. Finally, the computerized NNN system itself might be a factor due to its design or the selected content for inclusion for NNN linkages in the systems could influence the results of the study. Sample Size for Outcome Change Scores In this study, 84% of patients had documented planned care. Only 61% of patients diagnosed with Acute Pain had at least two outcome ratings to calculate outcome change scores. Although the sample size for the overall analysis was sufficient, some study variables, such as date of first cancer diagnosis, stage of cancer and treatment, and education, had obviously reduced the sample size for analysis. Nurses should receive alerts at discharge to make a discharge outcome rating to ensure quality data. Study Design and Tools SNTs in EHRs for Oncology Population Although using guided planned care embodies evidence-based practice, it is possible that nurses may encounter a patient problem that could not be addressed by

209 192 current developed care plans. For example, an oncology patient may have a unique need that had not been covered by the current SNTs in EHRs or the system has not been updated for this need. This leads to research bias. Another potential limitation is associated with accuracy of nursing diagnoses or their linkages of NNN in the sample, which was not considered in this study. This is because no other further information for patients conditions was documented in the plan of care for patients. The researcher checked that each linkage of NNN was logically placed. The nursing staff in the study hospital were highly educated (bachelor degree in nursing) and were well-trained in using SNTs for years. The current nursing information system using NNN in the study hospital had also been implemented since May, 2009 and nurses were provided with intensive continuing education and updates as modification in the system have occurred. This study did not focus on accuracy of nursing diagnosis based on clinical conditions. However, the potential errors may be lower as nurses from different work shifts planned the same care on the same patients. Another problem reported in the study was the lack of a clear dose of a nursing intervention in the care plan data in the current system. The dose of a nursing intervention is defined as amount and frequency of interventions. The dose of a nursing intervention was concerned because the improvement of outcomes can be accurately measured by the dose of interventions delivered. The transparence of nursing care then can be described in a scientific way with how much efforts need to be performed to reach the expected outcome. Moreover, nursing documentation was not linked to nursing planned care in the system, causing the confirmation of actual interventions to be even more difficult. This is a common weakness of current EHRs in terms of nursing applications and needs to be corrected by

210 193 vendors developing nursing information systems for nursing. Care planned must be linked to care delivered. Study Variables Data collection was retrospective. Factors associated with nursing planned care that were not documented would be impossible to be collected, such as the exact duration of each nursing intervention, rather than a start date and an end date. Some covariates relevant to outcome change scores were not collected in the study. For example, the study did not include medication information, which was potentially an important inference for Pain Management. The inference of medication control was not included as covariate for patients with Pain Level for Acute Pain, which may influence the outcome change scores. Future studies should include these variables. Implications for Nursing Practice The results of the study contributed to defining nursing knowledge of oncology care. The frequently used linkages of NNN in the study should be acknowledged by oncology nurses. The discrepancy between less frequently used linkages than expected for some nursing diagnoses, interventions and outcomes found in the study need further study to identify the issues. Some retired NNN terms were still available in this hospital s information system and unexpected nursing diagnoses, interventions, or outcomes are still in use. This reveals a problem. The organization may not be aware of the changes, updates may not have occurred in the EHR system at the time of the study, planned continuing education for nurses focused on the changes may not have happened at the time of the study, or an appropriate SNT has not been developed to replace the retired one.

211 194 It also can serve as a good example for training students critical thinking skills, helping them recognize unusual nursing diagnoses, nursing interventions, and outcomes. To identify frequently or infrequently used diagnoses, interventions, and outcomes, assistant nurse managers or administrators can assign patients to nurses and expect quality care considering the characteristics of patients, type of unit, age, and treatment that may influence their length of stay (LOS). Nurses can also use common outcome change scores for this population as a reference in decision-making. The discrepant findings in nursing diagnoses, interventions, and outcomes guide nurses in practice settings in new directions as needed. For example, psychosocial aspects of care may be perceived by nurses as less of a concerned in acute settings; however, in the concept of care continuum, the hospitalization period is the best time to holistically evaluate the patient s planned care. In conclusion, the recognition of nursing diagnoses, nursing interventions, and patient- sensitive patient outcomes including psychosocial needs is important to describe oncology nursing care using SNTs. This study provided a picture of current oncology nursing care, the demographics of patients on oncology specialty units and the core nursing process components (diagnoses, interventions, and outcomes) for patients receiving cancer care. This study also offered a valuable evaluation of oncology nursing care by examining the relationship between outcome score changes and patient characteristics and the association between outcomes score changes and cost-related variables, such as LOS using a large sample of patients. Additionally, the study addressed the linkages of currently used SNTs (NNN as examples) and provides an evaluation of SNTs in clinical practice and helps identify future development needs for SNTs in this specialty.

212 195 Implications for Nursing Education Use of the information in educational programs or training new oncology nurses in clinical practice bridge the gap of from school training into practice in a real world, especially in an organization using SNTs in the EHRs. The frequently used SNTs are a good resource for students and nurses obtaining knowledge in this specialty. The frequently used SNTs, such as NANDA-I diagnoses, NIC interventions, and NOC outcomes and their linkages (NNN) found in the study should be included in academic education and continuing education in oncology nursing. Nursing students must receive content on SNTs in their educational programs in order to recognize the importance of SNTs in EHRs as positioning nursing in new era of evidence-based practice. Nursing students need to learn about nursing through concepts that are fundamental to SNTs and thus decreasing the gap between the gap preventing a gap between education and practice as EHRs becomes mandatory in healthcare settings.. The use of SNTs also strengthens nursing students clinical reasoning skills. The OPT model was an example of applying NNN to clinical reasoning. A proper training program and continuing education of the NNN may most likely contribute to highly achievement of selecting correct NNN links in EHRs. The successful use of SNTs in EHRs in the study site can be an exemplar for other organizations in seeking the same innovation to achieve the Meaningful Use (MU) in the implementation of EHRs. Implications for the Health Care System and Health Policy Nursing leaders and administrators should address the need for electronic SNTs in their organizations. Governmental and non-governmental organizations should collaborate to promote resource integration in education, research, and the health care

213 196 industries. Stakeholders in the healthcare system should recognize the importance of SNTs in EHRs in providing cost-effective research potentially preventing nursing hours wasted in documentation and communication by investing in creating advanced nursing work environments with advanced nursing information and documentation systems. The findings on the outcome change scores of the study hopefully provided an initial insight for decision-makers for further policy changes, such as investing in advancing nursing information systems and providing a better nursing work environment to assure quality patient care. Recommendations for Future Research Use of Different Resources Use of different resources, such as patient data, nursing surveys, and observation from researchers, benefits in developing a more comprehensive sets of SNTs. It also fills the gap of the perspectives of nursing care using SNTs in EHRs between use in clinical practice and in research. In addition, each SNT may be developed for different purposes or different target populations. The NNN is designed to be able to describe nursing care in all settings and all populations (Johnson et al., 2011). To enable SNTs to appropriately describe nursing care in each specialty, SNTs have to be validated through patient data and nursing surveys in each specialty and in different care settings. The findings in the study using patient data provide evidence of the use of SNTs in EHRs describing nursing care in specific oncology units. This is an initial step for future studies examining a specific benefit of SNTs in EHRs in order to provide evidence for previous inconsistent findings. Conducting a survey of nursing staff in these oncology units is critical to evaluate current perspectives of nursing staff and their knowledge basis of their practice

214 197 expertise. A survey of these nurses or a future study should collect relevant information to see if a need for a new nursing diagnosis, a new nursing intervention, and a new nursing sensitive patient outcome as SNTs is needed. The findings will help nursing classification experts develop appropriate SNTs to describe nursing care for their specialties, such as common nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes for oncology nursing care. It suggests that the mechanism of retiring a nursing diagnosis, a nursing intervention, and a nursing-sensitive patient outcome may be required to be established by experts and clinical practical perspective as well. An ethnography methodology in the organization is an alternative to explore nursing planned care, especially audit for data quality or accuracy of the NNN linkages. It suggests organizational studies should consider this design (Brink & Wood, 1998). In summary, the completeness of SNTs for every population requires validation from patient data, surveys of nurses, and observation from researchers focused on effectiveness research. SNTs in EHRs and Data Warehouses A user-friendly design of planned care using SNTs as a nursing documentation system to provide efficient communication through EHRs is necessary. Nursing decision support systems with reminders for identifying patients who have been admitted longer than 24 hours without planned care is recommended. The use of structured nursing documentation using SNTs benefits organizations to assist in developing data warehouses.

215 198 Modify Study Design Inclusion of Different Populations in Multiple Sites and Other Covariates In future studies, data collection in different organizations using SNTs should be studied so that generalization of findings can be possible. Moreover, both micro-andmacro systems have to be considered in the research designs for the subject matters. On an individual level, some covariates relevant to alter outcome ratings have to be taken into consideration as critical to decision-making for the desired outcomes. For example, medication information and difference of individual threshold of pain may alter their outcome ratings for Pain Level. Individuals with different level of functional status may also alter their optimum range of outcome ratings for relevant NOC outcomes as baseline and through the care continuum. On a unit level, the type of specialty as found in the study was associated with LOS and outcome change scores for Pain Level for patients with Acute Pain. At the system level, nursing information systems or relevant policy, such as a template for a specific nursing care can influence plans of care and how they are addressed in EHRs. Donabedian s Structural-Process- Outcome Model provides a framework for this type of study. Inclusion of Dose of Nursing Intervention In order to accurately evaluate nursing care, the dose of nursing interventions should be measured and documented as a part of hospital standards. This will enable researchers to measure nursing-sensitive patient outcomes based on the amount of nursing care. A better-designed nursing information and documentation system is still required. However, the effort of linking the amount of nursing interventions and their association with improvement based on the nursing activities is an initial step that

216 199 evidence of Meaningful Use (MU) has emphasized in health care industry in recent years. These data are essential for comparison across organizations to determine why some organizations are producing better outcomes for patients than others. Differences in practice patterns (interventions) need to be a part of future research to build the knowledge base of nursing. Development of Optimum Ranges for Outcome Ratings To establish an optimum range of outcome ratings at admission, at discharge, and outcome change scores based on relevant information for a specific population would assist nurses in decision making for appropriate outcome expectation for patient care. Outcome change scores would be more accurately interpreted when relevant information is concerned, such as functional status. Additionally, based on disease process, maintaining the outcome ratings would become the maximum of nursing care that is expected, especially for a population, such as cancer patients, whose optimum outcome score may not range from one to five and whose outcome change scores may fluctuate as the disease process or because of individual health conditions (functional status). Therefore, disease process should be considered and an index for such special situations should be established for nurses for clinical-decision making. Pattern of NOC Outcome Change Scores Future studies should explore the pattern of change scores during hospitalization, especially for those patients have LOS.

217 200 Compatibility of NOC Outcome Ratings and Other Scales The fact is that a variety of scales are used to measure very similar things. The scales in each specialty may provide unique functions to measure their interest in certain variables. However, not all of them use a five-likert scale as NOC ratings. It is challenging to have all these commonly used scales in EHRs due to their variations and unstructured format. Therefore, a new strategy to validate the indicators of NOC and its five-likert scale can represent the same function as current scales used in each specialty. Future studies should focus on compatibility of a variety of scales and a potential communication between SNTs and these common used scales in each specialties or how SNTs can benefit from these current scales. Another consideration is how nursing information systems can benefit nurses daily workload. The advanced nursing information system is expected to save nursing time, for example, by automatically retrieving relevant patient information that is required for nursing assessment and reduce the repetitive documentations in EHRs. It is also expected to have reminder functions in order to provide nurses with decision support. The advanced nursing information system is expected to allow nurses to visualize each outcome rating and easily notice if there is a dramatic change in patient condition. The linkages of NANDA-I, NIC and NOC in EHRs provides a potential tool in nursing information systems and serves as an evidence-based decision support system for nurses and can enhance the quality of patient care. This is one of the first studies to identify NNN linkages focused on oncology patients. It is critical to study NOC outcomes and its compatibility with other commonly used quality indicators. The demand of a good measure should account for ease of use

218 201 and prevent nurses documenting the same things in two different formats. It may not be realistic to change current scales into 1 to 5 scales. However, NOC outcomes provide potential development of needed indicators in 1to 5-point Linker scale for each specialty. Conclusion NANDA-International, the Nursing Interventions Classification, and the Nursing Outcomes Classification describe nursing planned care for oncology units. The benefits of using NNN include identifying the common patient problems, symptoms, interventions, outcomes and their evaluation based on measurable indicators with a oneto-five Likert measurement scale. The study helps validate the common linkages of NNN in the oncology population. The study of linkages of NNN benefit research effectiveness by exploring the most effective interventions for outcome improvement based on a specific diagnosis. It also benefits exploring patterns of nursing diagnoses, nursing interventions and outcomes, especially for this population and other populations as well. For example, the exploration of patterns of nursing diagnoses, which identified patient healthcare problems, including symptoms are helpful for the newly emerging knowledge of symptom clusters. The discovery of new patterns of interventions is also helpful for clinical decision-making for nurses. The pattern of outcomes and their outcome changes scores would be an index for building a baseline standard care for a specific population. For example, the optimum outcome ratings may not be five as their disease processes and health status deteriorates, the desired rating may change to a lower rating. Therefore, the range of outcome ratings is expected in different stages of cancer. They may not always be expected to reach a rating of 5 and the target rating may be modified in different

219 202 stages as the disease advanced. In conclusion with these changes nursing diagnoses and interventions may change as well. Pain was the dominant concept in the nursing care provided to all oncology patients but only 78% of the cancer patients were diagnosed to have Acute Pain which was higher than 70% reported by 2004 ONS survey (Mallory & Thomas, 2004). For the 2,237 patients with a diagnosis of Acute Pain and the outcome Pain Level, 39% of the patients had no change in their outcome score at discharge, 12% had negative changes, and 49% had positive changes. Acute Pain is an area of great emphasis in the treatment of patients with cancer and these data suggest that improvements in the assessment of pain need to be reinforced in the care planning process. Risk for Infection was the most frequent nursing diagnosis for patients in the Adult Leukemia and Bone Marrow Transplant Unit. Hospitals should provide continuing education on infection control for these nurses. The results of this study demonstrate that cancer patients admitted to specialty units tend to have similar nursing diagnoses, interventions, and outcomes regardless of the type of cancer diagnosis. This study was the first step in establishing core diagnoses, interventions, and outcomes for oncology nurses using actual patient data. However, it is crucial to further investigate how pain is managed by oncology nurses by also examining outcome change scores. In future research, in order to use the data to maximum, combing other resources available, such as the National Database of Nursing Quality Indicators (NDNQI) data, can be useful in identifying other issues in nursing care. For example, a comparison of time spent on the planned NIC interventions using suggested time indicated from NIC (Nursing Interventions Classification) (Bulechek & Dochterman,

220 ), and the HPPD (Hour Per Patient Day) from administrative data, can be a focus for future studies. Knowledge about current practice in oncology using SNTs that links with different datasets, such as tumor registries and the National Database of Nursing Quality Indicators (NDNQI) database will help establish the core diagnoses, interventions, and outcomes for oncology nurses necessary to guide the evaluation of nurse competencies. The study plays an important role to evaluating current nursing planned care in oncology units using SNTs. The findings of the study provide knowledge of oncology care in the perspective of classification and EHRs. The lesson the author obtained for data retrieval also provides an insight to evaluate the nursing information system for documentation for this study hospital. This experience and findings of the study are further evidence of the need to advance the current underdeveloped nursing information system. First, the data retrieval experience revealed a demand to redesign the nursing information system to enable automatic data retrieval and storage for clinical practical and research purposes. The findings of the study would be a base standard to develop an index of standard nursing planned care and advanced nursing decision support systems for the oncology population by using the common linkages of NNN and their expected admission outcome ratings, discharge outcome rating, and outcome change scores. A replication of the study in different organizations is needed to refine nursing core standards and practices across patient population. Summary This study identifies oncology nursing knowledge using NANDA-I, NIC and NOC. The study also guide nursing administrator design continuing education programs for their nurses and build up a commonly used planned care for novice nurses. The

221 204 findings of the study can also provide useful educational resources for nursing students learning oncology nursing care and training their clinical thinking skills by using the common used NNN linkages. This study also identifies common range of NOC outcome ratings and NOC change scores for oncology patients. This helps nurses to plan an appropriate care as knowing the optimum range of this population. The results need to be replicated in multiple sites to enable the generalizability of the findings of the study. Future research using SNTs in EHRs should be conducted to describe the care provided by other specialties.

222 APPENDIX A DEFINITIONS 205

223 206 Definitions Variable Variable Definition Operational Definition Variable Type Characteristics of Patient Demographics Date of Birth Date of Birth mm/date/year Date Gender The behavioral 0 = Female Categorical cultural or psychological traits 1 = Male (Binary) typically associated with one sex. Race A group of people united by certain characteristics. 0 = Caucasian 1 = African- American 2 = Asian 3 = American Indian 4 = Other Categorical (Nominal) Data Resource EMR (Electronic Medical Records) EMR Marital Status Years of Education Patient s Employment Status Marital status indicates whether the person is married divorced or widowed. Education level is defined as the highest degree or diploma that has been completed. Activity pursued as a livelihood; An activity to which one devotes time. 0 = Married 1 = Single 2 = Divorced 3 = Widowed 4 = Separated 5 = Life partner Categorical (Nominal) EMR Years Continuous EMR 0 = Retired 1 = Unemployed/Not retired 2 = Disabled 3 = Employed/Servic e 4 = Homemaker 5 = Other Categorical (Nominal) EMR

224 207 Table Continued Medical Diagnosis Primary Site of Tumor Date Of First Cancer Diagnosis Stage of Cancer Severity of Illness Disease Information The Primary Medical ICD Code diagnosis came from International Classification Disease-9 th Version Clinical Modification codes (ICD-9-CM) (Public Health Service and Health Care Financing Administration 1994) The Primary site of ICD Code tumor Date of first diagnosed as cancer The stage of a cancer is a descriptor (usually numbers I to IV) of how much the cancer has spread and influences treatment decisions and prognosis. Staging is determined based on three measures contained in the pathology report: T (Tumor Size) N (Lymph Node Status) and M (Metastasis). A rating assigned to measure organ system loss of function or physiological decompensation. The All Patient Refined Diagnosis Related Group (APR-DRG) was used. Categorical (Nominal) EMR Categorical (Nominal) Tumor Registry mm/date/year Date Tumor Registry (Depend on the hospital coding) 0 = Stage I-III 1 = Stage IV Integral; 1 = Mild 2 = Moderate 3 = Major 4 = Severe Categorical (Ordinal) Categorical (Ordinal) Tumor Registry EMR

225 208 Table Continued Comorbidity Admission Date to an Oncology Unit Initial Site at Admission Type of Unit Clinical Conditions that exist before admission which are not related to the hospitalization; however they are likely to contribute mortality and resource utility. Binary; 0 = No comorbidity 1= Yes at least one comorbid condition Hospitalization Information Dates at admission to one of the four oncology units in the study year The site from which the patient was admitted to the hospital. Type of the four oncology units that patients admitted to Each specific date at admission to any oncology unit in the study year 0 = Other units in the hospital 1 = Other hospital 2 = Care facility and 3 = Home or other routine admission 1 = Gynecology, Oral Surgery, and Otolaryngology Unit 2 = Hematology/Onc ology and Palliative Care Unit 3 = Medical- Surgical Oncology 4 = Adult and Bone Marrow Transplant Unit Categorical (Binary) Date(s) Categorical (Nominal) Categorical (Nominal) EMR EMR EMR EMR

226 209 Table Continued Admission Date to Hospital Admission Date to Oncology Unit Discharge Date from an Oncology Unit Length of Stay (LOS) Discharge Status MS-DRG Date of admission to hospital in the study period Date of Admission to any oncology unit in the study period Date at discharge from any oncology unit in the study period The number of days that a patient stayed in an inpatient facility during a single episode of hospitalization. The site to which the patient was referral at discharge. Each specific date of admission to hospital in the study period Each specific date of admission date to any oncology unit in the study period Each specific date at discharge from any oncology unit in the study period Length of stay is the count of days between patient admission day and patient discharge day as a continuous variable. (Discharge Date Admission Date) 0 = Home or self-care 1 = Home with home health care 2 = Other facilities 3 = Deceased Cost-related Variables Medical-Service The categorical Disease-Related variable was Group is defined as a listed in system to classify appendix. hospital cases into one of approximately 500 groups for resource utility. Date(s) Numerical (Continuous) Categorical (Nominal) Categorical (Nominal) EMR EMR EMR EMR

227 210 Table Continued Mortality Types of Insurance The number of deaths within a particular group or area Health insurance is insurance that pays for all or part of a person s health care bills. The types of health insurance are group health plans individual plans workers compensation and government health plans such as Medicare and Medicaid. Cancer Surgery Operation for the treatment of cancer Treatment Death divided 30 day hospitalization 0 = No insurance/self Pay 1 = Medicaid 2 = Medicare 3 = Other Insurance 0 = No surgery 1 = surgery Numerical (Continuous) Categorical (Nominal) Categorical EMR EMR Tumor Registry Radiotherapy Radiotherapy for treatment of cancer Categorical; 0 = No radiotherapy 1 = radiotherapy Categorical Tumor Registry Chemotherapy Other Type of Medical Treatments Chemotherapy for treatment of cancer Any other medical treatments related to cancer, such as hormone therapy or immunotherapy, or other combinations. 0 = No chemotherapy 1 =chemotherapy List of treatment type Categorical Categorical (Nominal) Tumor Registry Tumor Registry

228 211 Table Continued Nursing Diagnosis (NANDA-I) Nursing Intervention (NIC) Standard Nursing Terminologies (SNTs) Multi-level NANDA-I: Nursing diagnosis is defined as a clinical judgment about individual family or community responses to actual or potential health problems / life processes. A nursing diagnosis provides the basis for selection of nursing interventions to achieve outcomes for which the nurse is accountable (NANDA-I p.419). Nursing interventions delivered to enhance patient outcomes. These labels were captured in NIC format. NIC: An intervention is defined as any treatment based upon clinical judgment and knowledge that a nurse performs to enhance patient/client outcomes (Dochterman & Bulechek 2008, p. 3). Categorical; (Multi-level). Categorical (Nominal) Categorical (Nominal) Nursing Information System/Ma nual Retrieval Nursing Information System/Ma nual Retrieval

229 212 Table Continued Nursing- Sensitive Patient Outcomes (NOC) Linkages of NANDA-I NIC and NOC NOC: Nursingsensitive patient outcome is defined as An intervention family or community state behavior or perception that is measured along a continuum in response to a nursing intervention(s). Each outcome has an associated group of indicators that are used to determine patient status in relation to the outcome In order to the measurement the outcome requires identification of a series of more specific indicators (Moorhead Johnson & Maas 2004, p. 38) Linkages of NANDA-I NIC and NOC are taken to the whole combination of the nursing diagnoses nursing interventions and nursing-sensitive patient outcomes in individuals. This describes how SNLs are combined together as a set of complete nursing documentation for nursing activities. Multi-levels; Multi-levels; Categorical (Nominal) Categorical (Nominal) Nursing Information System/ Manual retrieval Nursing Information System/ Manual retrieval

230 213 Table Continued Measurable Nursing-Sensitive Patient Outcome Variables Nursing Score changes on the Sensitive 5 point rating of NOC Patient for per unique type of Outcome NOC. The intensity Rating (NOC and duration were Score Rating) documented as well. Changes in Nursing Sensitive Patient Outcome Rating (NOC Score Rating) Duration of first and last measurement of Nursing Sensitive Patient Outcome Rating Changes in Nursing Sensitive Patient Outcome Rating (NOC Score Rating) on the 5 point rating for per unique type of NOC. The duration between the first and last measurement of nursing-sensitive patient outcome rating. Nursing Sensitive Patient Outcome Rating (NOC Score Rating) on a 0 to 5 scale. Nursingsensitive patient outcome rating for each NOC labels both at first and last measurement. Final score minus initial score of NOC. Time of last measurement minus first measurement of nursing-sensitive patient outcome rating. Categorical (Ordinal) Numerical (Discrete) Numerical (Continuous) Nursing Information System/ Manual retrieval Nursing Information System/ Manual retrieval Nursing Information System/ Manual retrieval

231 APPENDIX B NANDA-I DIAGNOSIS: ACUTE PAIN 214

232 215 Acute Pain (00132) (1996) Domain 12: Comfort Class 1: Physical Comfort Definition: Unpleasant sensory and emotional experience arising from actual or potential tissue damage or described in terms of such damage (International Association for the Study of Pain); sudden or slow onset of any intensity from mild to severe with an anticipated or predictable end and a duration of less than 6 months Defining Characteristics Change in Appetite Change in blood pressure Change in heart rate Change in respiratory rate Coded reports (e.g., use of pain scale) Related Factors: Diaphoresis Distraction behavior (e.g., pacing, seeking out other people or activities, repetitive activities) Expressive behavior (e.g., restlessness, moaning, crying, vigilance, irritability, sighing) Facial mask (e.g., eyes lack luster, beaten look, fixed or scattered movement, grimace) Guarding behavior Narrowed focus (e.g., altered time perception, impaired thought process, reduced interaction with people and environment) Observed evidence of pain Position to avoid pain Protective gestures Pupillary dilation Reports pain Self-focused Sleep pattern disturbance Injury agents (e.g., biological, chemical, physical, psychosocial)

233 APPENDIX C NIC INTERVENTIONS: PAIN MANAGEMENT 216

234 217